Tag Archives: hardware

Open source SystemVerilog tools in ASIC design

Open source hardware is undeniably undergoing a renaissance whose origin can be traced to the establishment of RISC-V Foundation (later redubbed RISC-V International). The open ISA and ecosystem, in which Antmicro participated since the beginning as a Founding member, has sparked many open source CPU implementations, new tooling, methodologies, and trends which allow for more collaborative and software driven design.

Many of those broader open hardware activities have been finding a home in CHIPS Alliance, an open source organization we participate in as a Platinum member alongside Google, Intel, Western Digital, SiFive and others, whose goals explicitly encompass:
  • creating and maintaining open source ASIC and FPGA design tools (digital and analog)
  • open source core and uncore IP
  • interconnects, interoperability specs and more
This is in perfect alignment with Antmicro’s mission—as we’ve been heavily involved with many of the projects inside of and related to CHIPS providing commercial support, engineering services, and assistance in practical adoption for enterprise deployments.

As of this time, a range of everyday design, development, testing, and verification tasks are already possible using open source tools and components and are part of our and our customer’s everyday workflow. Other developments are within reach given a reasonable amount of development, which we can provide based on specific scenarios. Others still are much further away, but with dedicated efforts inside CHIPS in which we are involved together with partners like Google and Western Digital, there is a pathway towards a completely open hardware design and verification ecosystem. This will eventually unlock incredible potential in new design methodologies, vertical integration capabilities, and education and business opportunities. Until then, Antmicro can help you with extracting practical value for many scenarios such as simulation, linting, formatting, synthesis, continuous integration and more.

Building a SystemVerilog ecosystem in CHIPS

Some of the challenges towards practical adoption of open source in ASIC design have been related to the fact that a significant proportion of advanced ASIC design is done in SystemVerilog, a fairly complex and powerful language in its own right, which used to be poorly supported in the open source tooling ecosystem. Partial solutions like SystemVerilog to Verilog converters or paid plugins existed, but direct support lagged behind, making open source tools for SystemVerilog a difficult sell previously.

This has been fortunately changing rapidly with a dedicated development effort spearheaded by Google and Antmicro. Projects in this space include Verible, Surelog, UHDM and sv-tests that we have been developing, as well as integrating with existing tools like Yosys, Verilator under the umbrella of the SymbiFlow open source FPGA project, and which are now officially being transferred into the CHIPS Alliance to increase awareness and build a broader SystemVerilog ecosystem.

In this note, we will walk you through the state of the art in new SystemVerilog capabilities in open source projects, and invite you to reach out to see how CHIPS Alliance’s SystemVerilog projects can be useful to you today or in the near future.

A walk through the state of the art in new SystemVerilog capabilities in open source projects

Verible

The Verible project originated at Google; its main mission is to make SystemVerilog easily and quickly parsable for a wide variety of applications mostly focusing on developer tools.

Verible is a set of tools based on a common SystemVerilog parsing engine, providing a command line interface which makes integration with other tools for daily usage or CI systems for automatic testing and deployment a breeze.

Antmicro has been involved in the development of Verible since its initial open source release and we now provide a significant portion of current development efforts, helping adapt it for use in various open source projects or commercial environments that use SystemVerilog. One notable user is the security-focused OpenTitan project, which has driven many interesting developments and provides a good showcase of the capabilities being completely open source, well documented, fairly complex, and used in real applications.

Linter

One of the most common use cases for Verible is linting. The linter analyzes code for patterns and constructs that are deemed undesirable according to the implemented lint rules. The rules follow authoritative style guides that can be enforced on a project or company level in various SystemVerilog projects.

The rules range from simple ones like making sure the module name matches the file name to more sophisticated like checking variable naming conventions (all caps, snake case, specific prefix or suffix etc.) or making sure the labels after the begin and end statements match.

A full list of rules can be found in the Verible lint documentation and is constantly growing. Usage is very simple:

$ verible-verilog-lint --ruleset all core.sv 

core.sv:3:11: Interface names must use lower_snake_case naming convention and end with _if. [Style: interface-conventions] [interface-name-style]


The output of the linter is easy to understand, as the way issues are reported to the user is modeled after popular programming language compilers.

The linter is highly configurable. It is possible to select the rules for which the compliance will be checked, some rules allow for detailed configuration (e.g. max line length).

Rules can also be selectively waived in specific files or at specific lines or even by regex matching. In addition, some rules can be automatically fixed by the linter itself.

Formatter

The Verible formatter is a complementary tool for the linter. It is used to automatically detect various formatting issues like improper indentation or alignment. As opposed to the linter, it only detects and fixes issues that have no lexical impact on the source code.

The formatter also comes with useful helper scripts for selective and interactive reformatting (e.g. only format files that changed according to git, ask before applying changes to each chunk).

A toolset that consists of both the linter and the formatter can effectively remove all the discussions about styling, preferences and conventions from all pull requests. Developers can then focus solely on the technical aspects of the proposed changes.

$ cat sample.sv

typedef struct {

bit first;

        bit second;

bit

   third

        ;

  bit fourth;

bit fifth; bit sixth;

}

 foo_t;



$ verible-verilog-format sample.sv

typedef struct {

  bit first;

  bit second;

  bit third;

  bit fourth;

  bit fifth;

  bit sixth;

} foo_t;

Indexer

The Verible parser itself can be relatively easily used to perform many other tasks. One of the interesting use cases is generating a Kythe compatible indexing database.

Indexing a SystemVerilog project makes it very easy to collaborate on a project remotely. It is possible to navigate through the source code using nothing else than just a web browser.

The Kythe integration can be served on an arbitrary server, can be deployed after every commit in a project, etc. A showcase of the indexing mechanism can be found in our GitHub repository. The demo downloads the latest version of the Ibex core, indexes it, and deploys it to be viewed on a remote machine. The results can be viewed on the example index webpage.

The demo downloads the latest version of the Ibex core, indexes it, and deploys it to be viewed on a remote machine. The results can be viewed on the example index webpage.

Indexing is widely adopted for many larger open source software projects.

Thanks to Verible, it is now possible to do the same in the world of open source HDL designs, and of course private, company-wide deployments like this are also possible.

Surelog and UHDM

SystemVerilog is a powerful language but also complex. So far no open source tools have been able to support it in full. Implementing it separately for each project such as the Yosys synthesis tool or the Verilator simulator would take a colossal amount of time, and that’s where Surelog and UHDM come in.

Surelog, originally created and led by Alain Dargelas, aims to be a fully-featured SystemVerilog 2017 preprocessor, parser, and elaborator. It’s a modern tool and thus follows the current version of the SV standard without unnecessary deviations or legacy baggage.

What’s interesting is that Surelog is only a language frontend designed to integrate well with other tools—it outputs an elaborated design in an intermediate format called UHDM.

UHDM stands for Universal Hardware Data Model, and it’s both a file format for storing hardware designs and a library able to manipulate this format. A client application can access the data using VPI, which is a standard programming interface for SystemVerilog.

What this means is that the work required to create a SystemVerilog parser only needs to be done once, and other tools can use that parser via UHDM. This is much easier than implementing a full SystemVerilog parser within each tool. What’s more, any improvements in the unified parser will provide benefits for all client applications. Finally, any other parser is free to emit UHDM as well, so in the future we might see e.g. a UHDM backend for Verible.

Just like in Verible’s case, both Surelog and UHDM have recently been contributed into CHIPS Alliance to drive a broader adoption. We are actively contributing to both projects, especially around the integrations with tooling such as Yosys and Verilator, and practical use in open source and customer projects.

Recent Antmicro contributions adding UHDM frontends for Yosys and Verilator enabled Ibex synthesis and simulation. The complete OpenTitan project is the next milestone.

The Surelog/UHDM/Yosys flow enabling SystemVerilog synthesis without the necessity of converting the HDL code to Verilog is a great improvement for open source ASIC build flows such as OpenROAD’s OpenLane flow (which we also support commercially). Removing the code conversion step enables the developers to perform e.g. circuit equivalence validation to check the correctness of the design.

More information about Surelog/UHDM and Verible can be found in a dedicated CHIPS Alliance presentation that was recently given by Henner Zeller, Google’s Verible lead.

UVM is in the picture

No open source ASIC design toolkit can be complete without support for Universal Verification Methodology, or UVM, which is one of the most widespread verification methodologies for large-scale ASIC design. This has also been an underrepresented area in open source tooling and changing that is an enormous undertaking, but working together with our customers, most notably Western Digital, we have been making progress on that front as well.

Across the ASIC development landscape, UVM verification is currently performed with proprietary simulators, but a more easily distributable, collaborative and open ecosystem is needed to close the feedback loop between (emerging) open source design approaches and verification. Verilator is an extremely popular choice for other system development use cases but it has historically not focused on UVM-style verification. Other styles of verification, such as the very interesting and popular Python-based cocotb framework maintained by FOSSi Foundation, have been enabled in Verilator. But support for UVM, partly due to the size and complexity of the methodology, has been notably absent.

One of the features missing from Verilator but needed for UVM is SystemVerilog stratified scheduling, which is a set of rules specified in the standard that govern the way time progresses in a simulation, as well as the order of operations. A SystemVerilog simulation is divided into smaller steps called time slots, and each time slot is further divided into multiple regions. Specific events can only happen in certain regions, and some regions can reoccur in a single time slot.

Until recently, Verilator had implemented only a small subset of these rules, as all scheduling was being done at compilation time. Spearheading a long-standing development effort within CHIPS Alliance, in collaboration with the maintainer of Verilator, Wilson Snyder, we have built is a proof-of-concept version of Verilator with a dynamic scheduler, which manages the occurrence of certain events at runtime, extending the stratified scheduling support. More details can be found in Antmicro’s presentation for the inaugural CHIPS Alliance Deep Dive Cafe Talk.

Another feature required for UVM is constrained randomization, which allows generating random inputs to feed to a design in order to thoroughly test it. Unlike unconstrained randomization, which is already provided by Verilator, it allows the user to specify some rules for input generation, thus limiting the possible value space and making sure that the input makes sense. Work on adding this to Verilator has already started, although the feature is still in its infancy. There are many other features on the roadmap which will eventually enable practical UVM support—stay tuned with our CHIPS Alliance events to follow that development.

What next?

Support for SystemVerilog parsers, for the intermediate format, and for their respective backends and integrations with various tooling, as well as for UVM is now under heavy development. If you would like to see more effort put into a specific area, reach out to us at [email protected]. Antmicro offers commercial support services to extend the flows we’ve briefly presented here to various practical applications and designs, and to effectively integrate this approach into people’s workflows.

Adding to this our cloud expertise, Antmicro customers can benefit from a complete and industry-proven methodology scalable between teams and across on-premise and cloud installations, transforming chip design workflows to be more software-driven and collaborative. To take advantage of open source solutions with tools like Verilator, Yosys, OpenROAD and others - tell us about your use case and we will see what can be done today.

If you are interested in collaborating on the development of SystemVerilog-focused and other open hardware tooling, join CHIPS Alliance and participate in our workgroups and help us push innovation in ASIC design forward.

Originally posted on the Antmicro blog.

By guest author Michael Gielda, Antmicro, and Tim Ansell, Software Engineer

Pixel Buds A-Series: Rich sound, iconic design, just $159


When we first introduced our truly wireless Pixel Buds, we were most excited about how such a small product could pack so much functionality. Now, we’re making that same premium sound quality, along with hands-free help from Google Assistant and real-time translation, available at an even more affordable price. 
Introducing Pixel Buds A-Series: rich sound, clear calls and Google helpfulness, all in a low-profile design – for just $159. 

A premium audio experience 
Our research shows that most people describe great sound as full, clear and natural. This is what guides our audio tuning process and shows up in other devices, like Nest Audio. And Pixel Buds A-Series are no exception. Custom-designed 12mm dynamic speaker drivers deliver full, clear and natural sound, with the option for even more power in those low tones with Bass Boost. 
To experience the full range of the speaker’s capabilities, especially in the low frequencies, a good seal is essential. We’ve scanned thousands of ears to make Pixel Buds A-Series fit securely with a gentle seal. In order to keep the fit comfortable over time, a spatial vent reduces in-ear pressure. 
Each earbud also connects to the main device playing audio, and has strong individual transmission power, to keep your sound clear and uninterrupted. 
Sound quality can also be affected by your environment. The new Pixel Buds A-Series come with Adaptive Sound, which increases or decreases the volume based on your surroundings. This comes in handy when you're moving from the quiet of your home to somewhere noisy like a city street, or while jogging past a loud construction site. 
And your calls will have great sound too. To make sure your calls are as clear as they can be, Pixel Buds A-Series use beamforming mics to focus on your voice and reduce outside noise, making your calls crystal clear (though of course, overall call quality depends on signal strength, environment, network, and other factors). Once your call is over, quickly get back to your music with a simple “Hey Google, Play my music.” 

Stylish and hardworking 
For Pixel Buds A-Series, we wanted to bring back the iconic Clearly White, but added a twist with new grey undertones. 
Pixel Buds’ design is inspired by the idea that great things can come in small packages: Pixel Buds A-Series include up to five hours of listening time on a single charge or up to 24 hours using the charging case. And with the ability to get a quick charge — about 15 minutes in the case gives you up to three hours of listening time — you can keep listening anywhere.1 
They’re comfortable enough for those long listening sessions, and don’t worry if some of that time is devoted to a sweaty workout or a run in the rain: The earbuds are also sweat and water-resistant.2 

Hands-free access to the best of Google 
Google Assistant is built right into the Pixel Buds A-Series. You can get quick hands-free help to check the weather, get an answer, change the volume, or have notifications read to you with a simple “Hey Google.” 

Added accessories 
To help protect your new Pixel Buds A-Series, there is now the Tech21 EvoSlim — a lightweight case to shield your smallest tech from drops and scratches. It is made with a built-in microbe-reducing formula and has an easy-to-attach carabiner to help keep your Pixel Buds A-Series safe and close to hand. Available on the Google Store soon. 
Pixel Buds A-Series are now available for pre-order in Australia from the Google Store, arriving to customers from August 25. Pixel Buds A-Series will be available online from August 25 at JB Hi-Fi, Harvey Norman, and Officeworks, and available at Optus and Vodafones later this year. Pixel Buds A-Series will also be available online at Telstra from August 27. For more country availability and waitlist options, visit g.co/pixelbudsaseries


1 All listening times are approximate and were measured using music playback with pre-production hardware and software, with fully charged Pixel Buds A-Series and case, and other features disabled. Case is used to recharge Pixel Buds A-Series when their batteries are depleted. Charging times are approximate. Use of other features will decrease battery life. Battery life depends on device, features enabled, usage, environment and many other factors. Actual battery life may be lower. 
2 Pixel Buds A-Series (earbuds only) have a water protection rating of IPx4 under IEC standard 60529. Water resistance is not a permanent condition and may be compromised by normal wear and tear, repair, disassembly, or damage. 

New from Google Nest: The latest Cams and Doorbell are coming

Google Nest’s mission is to build products that make a more helpful home. All of this starts with helping you understand what’s happening within the walls of your home and outside of it. 

One of Nest’s first goals was to simplify home security, and it helped millions of people across the globe do this. So when we started dreaming up our next generation of cameras and doorbells, we wanted to incorporate the way the connected home — and your expectations — were heading. That included smarter alerts, wire-free options for installation flexibility, greater value and beautiful designs, plus enhanced privacy and security. We wanted our newest line to give you the most comprehensive set of intelligent alerts right out of the box, and easily work with your other Nest products, like displays. 

Today we’re introducing our next-generation Nest Cams and Doorbell: Google Nest Cam (battery) is our first outdoor/indoor battery-powered camera ($329); Google Nest Doorbell (battery) is our first battery-powered doorbell ($329). Learn more about 11 things to love about the new Nest Cam and Doorbell
Meet the new Google Nest Cam and Google Nest Doorbell

Then there’s Google Nest Cam with floodlight, our first connected floodlight camera ($549) and finally the second-generation Google Nest Cam (wired), a wired indoor camera and our most affordable Nest Cam ever ($169). 

We’ve heard how much people appreciate it when their Nest products all work well together. These new devices are no different. With the new Nest Cams and a display, you can keep an eye on the backyard from your kitchen and get alerts when the doorbell rings. Our new cameras are also fully integrated with the Google Home app. The Google Home app works with any compatible Android or iOS device, giving you access to all your compatible home devices in one place, anywhere and anytime. 

The new battery-powered Nest Cam and Nest Doorbell will go on sale on August 25, and are available for preorder today from the Google Store, JB Hi-Fi, Harvey Norman, Officeworks and The Good Guys. And for those who preorder, you can also secure an extra gift of a second-generation Nest Hub from selected retailers. 

Nest Cam with floodlight and the new wired indoor Nest Cam are coming soon. 

To learn more, visit the Google Store

Meet the new Nest Hub

Introducing the second-generation Nest Hub! Since we launched Google’s first smart display two years ago, it’s brought help to thousands of homes and we’ve been dedicated to exploring ways to make our devices even more helpful. 

The Nest Hub you love, but better 
The new Nest Hub’s speaker is based on the same audio technology as Nest Audio and has 50 percent more bass than the original Hub for a bigger, richer sound to fill any room with music, podcasts or audiobooks from services like YouTube Music and Spotify — or enjoy your favourite TV shows and movies with a subscription from providers like Netflix, Disney+ and Stan. With Quick Gestures, you can pause or play content at any time by tapping the air in front of your display. 
The new Nest Hub shows all your compatible connected devices in one place so you can control them with one tap. And with a built-in Thread radio, Nest Hub will work with the new connectivity standard being created by the Project Connected Home over IP working group, making it even simpler to control your connected home. 

Nest Hub is also full of help for your busy family. See your calendar, set timers, and create reminders with Family Notes, digital sticky notes to share chores and to-dos so everyone stays on track. 


New sleep features for better rest 
The Nest Hub has always helped you tackle the day; now, it can help you rest well at night. Many of us don’t get enough sleep, which is becoming the number one concern for adults when it comes to health and wellness. 
As people have started to recognise the need for better sleep, sleep trackers have continued to become a popular solution. But we wanted to offer an alternative way for people who may not want to wear something to bed to understand their sleep. 
We dug into the data, and because we also knew people felt comfortable with Nest Hub at their bedsides thanks to its camera-free design, we went to work. The result is Sleep Sensing, an opt-in feature to help you understand and improve your sleep — and is available as a free preview until next year. 
Sleep Sensing is completely optional with privacy safeguards in place so you’re in control: You choose if you want to enable it and there's a visual indicator on the display to let you know when it’s on. Motion Sense only detects motion, not specific bodies or faces, and your coughing and snoring audio data is only processed on the device — it isn’t sent to Google servers. You have multiple controls to disable Sleep Sensing features, including a hardware switch that physically disables the microphone. You can review or delete your sleep data at any time, and consistent with our privacy commitments, it isn't used for personalised ads. 
Even if you choose not to enable Sleep Sensing, you can still fall asleep and wake up easier with Nest Hub. The display dims to make your bedroom more sleep-friendly, and the “Your evening” page helps you wind down at night with relaxing sounds. When it’s time to wake up, Nest Hub’s Sunrise Alarm gradually brightens the display and increases the alarm volume. If you need a few more ZZZs, use Motion Sense to wave your hand and snooze the alarm. 


Sustainable design that matches any room 
The new Nest Hub will be available to Australians in two colours, to complement most rooms in the house: Chalk and Charcoal. It features an edgeless glass display that’s easy to clean and makes your Nest Hub an even more beautiful digital photo frame. And continuing our commitment to sustainability, Nest Hub is designed with recycled materials with its plastic mechanical parts containing 54 percent recycled post-consumer plastic. 

The second-generation Nest Hub is $149. It can be preordered online in Australia at the Google Store and other retailers from today.

Contactless Sleep Sensing in Nest Hub

People often turn to technology to manage their health and wellbeing, whether it is to record their daily exercise, measure their heart rate, or increasingly, to understand their sleep patterns. Sleep is foundational to a person’s everyday wellbeing and can be impacted by (and in turn, have an impact on) other aspects of one’s life — mood, energy, diet, productivity, and more.

As part of our ongoing efforts to support people’s health and happiness, today we announced Sleep Sensing in the new Nest Hub, which uses radar-based sleep tracking in addition to an algorithm for cough and snore detection. While not intended for medical purposes1, Sleep Sensing is an opt-in feature that can help users better understand their nighttime wellness using a contactless bedside setup. Here we describe the technologies behind Sleep Sensing and discuss how we leverage on-device signal processing to enable sleep monitoring (comparable to other clinical- and consumer-grade devices) in a way that protects user privacy.

Soli for Sleep Tracking
Sleep Sensing in Nest Hub demonstrates the first wellness application of Soli, a miniature radar sensor that can be used for gesture sensing at various scales, from a finger tap to movements of a person’s body. In Pixel 4, Soli powers Motion Sense, enabling touchless interactions with the phone to skip songs, snooze alarms, and silence phone calls. We extended this technology and developed an embedded Soli-based algorithm that could be implemented in Nest Hub for sleep tracking.

Soli consists of a millimeter-wave frequency-modulated continuous wave (FMCW) radar transceiver that emits an ultra-low power radio wave and measures the reflected signal from the scene of interest. The frequency spectrum of the reflected signal contains an aggregate representation of the distance and velocity of objects within the scene. This signal can be processed to isolate a specified range of interest, such as a user’s sleeping area, and to detect and characterize a wide range of motions within this region, ranging from large body movements to sub-centimeter respiration.

Soli spectrogram illustrating its ability to detect a wide range of motions, characterized as (a) an empty room (no variation in the reflected signal demonstrated by the black space), (b) large pose changes, (c) brief limb movements, and (d) sub-centimeter chest and torso displacements from respiration while at rest.

In order to make use of this signal for Sleep Sensing, it was necessary to design an algorithm that could determine whether a person is present in the specified sleeping area and, if so, whether the person is asleep or awake. We designed a custom machine-learning (ML) model to efficiently process a continuous stream of 3D radar tensors (summarizing activity over a range of distances, frequencies, and time) and automatically classify each feature into one of three possible states: absent, awake, and asleep.

To train and evaluate the model, we recorded more than a million hours of radar data from thousands of individuals, along with thousands of sleep diaries, reference sensor recordings, and external annotations. We then leveraged the TensorFlow Extended framework to construct a training pipeline to process this data and produce an efficient TensorFlow Lite embedded model. In addition, we created an automatic calibration algorithm that runs during setup to configure the part of the scene on which the classifier will focus. This ensures that the algorithm ignores motion from a person on the other side of the bed or from other areas of the room, such as ceiling fans and swaying curtains.

The custom ML model efficiently processes a continuous stream of 3D radar tensors (summarizing activity over a range of distances, frequencies, and time) to automatically compute probabilities for the likelihood of user presence and wakefulness (awake or asleep).

To validate the accuracy of the algorithm, we compared it to the gold-standard of sleep-wake determination, the polysomnogram sleep study, in a cohort of 33 “healthy sleepers” (those without significant sleep issues, like sleep apnea or insomnia) across a broad age range (19-78 years of age). Sleep studies are typically conducted in clinical and research laboratories in order to collect various body signals (brain waves, muscle activity, respiratory and heart rate measurements, body movement and position, and snoring), which can then be interpreted by trained sleep experts to determine stages of sleep and identify relevant events. To account for variability in how different scorers apply the American Academy of Sleep Medicine’s staging and scoring rules, our study used two board-certified sleep technologists to independently annotate each night of sleep and establish a definitive groundtruth.

We compared our Sleep Sensing algorithm’s outputs to the corresponding groundtruth sleep and wake labels for every 30-second epoch of time to compute standard performance metrics (e.g., sensitivity and specificity). While not a true head-to-head comparison, this study’s results can be compared against previously published studies in similar cohorts with comparable methodologies in order to get a rough estimate of performance. In “Sleep-wake detection with a contactless, bedside radar sleep sensing system”, we share the full details of these validation results, demonstrating sleep-wake estimation equivalent to or, in some cases, better than current clinical and consumer sleep tracking devices.

Aggregate performance from previously published accuracies for detection of sleep (sensitivity) and wake (specificity) of a variety of sleep trackers against polysomnography in a variety of different studies, accounting for 3,990 nights in total. While this is not a head-to-head comparison, the performance of Sleep Sensing on Nest Hub in a population of healthy sleepers who simultaneously underwent polysomnography is added to the figure for rough comparison. The size of each circle is a reflection of the number of nights and the inset illustrates the mean±standard deviation for the performance metrics.

Understanding Sleep Quality with Audio Sensing
The Soli-based sleep tracking algorithm described above gives users a convenient and reliable way to see how much sleep they are getting and when sleep disruptions occur. However, to understand and improve their sleep, users also need to understand why their sleep is disrupted. To assist with this, Nest Hub uses its array of sensors to track common sleep disturbances, such as light level changes or uncomfortable room temperature. In addition to these, respiratory events like coughing and snoring are also frequent sources of disturbance, but people are often unaware of these events.

As with other audio-processing applications like speech or music recognition, coughing and snoring exhibit distinctive temporal patterns in the audio frequency spectrum, and with sufficient data an ML model can be trained to reliably recognize these patterns while simultaneously ignoring a wide variety of background noises, from a humming fan to passing cars. The model uses entirely on-device audio processing with privacy-preserving analysis, with no raw audio data sent to Google’s servers. A user can then opt to save the outputs of the processing (sound occurrences, such as the number of coughs and snore minutes) in Google Fit, in order to view personal insights and summaries of their night time wellness over time.

The Nest Hub displays when snoring and coughing may have disturbed a user’s sleep (top) and can track weekly trends (bottom).

To train the model, we assembled a large, hand-labeled dataset, drawing examples from the publicly available AudioSet research dataset as well as hundreds of thousands of additional real-world audio clips contributed by thousands of individuals.

Log-Mel spectrogram inputs comparing cough (left) and snore (right) audio snippets.

When a user opts in to cough and snore tracking on their bedside Nest Hub, the device first uses its Soli-based sleep algorithms to detect when a user goes to bed. Once it detects that a user has fallen asleep, it then activates its on-device sound sensing model and begins processing audio. The model works by continuously extracting spectrogram-like features from the audio input and feeding them through a convolutional neural network classifier in order to estimate the probability that coughing or snoring is happening at a given instant in time. These estimates are analyzed over the course of the night to produce a report of the overall cough count and snoring duration and highlight exactly when these events occurred.

Conclusion
The new Nest Hub, with its underlying Sleep Sensing features, is a first step in empowering users to understand their nighttime wellness using privacy-preserving radar and audio signals. We continue to research additional ways that ambient sensing and the predictive ability of consumer devices could help people better understand their daily health and wellness in a privacy-preserving way.

Acknowledgements
This work involved collaborative efforts from a multidisciplinary team of software engineers, researchers, clinicians, and cross-functional contributors. Special thanks to D. Shin for his significant contributions to this technology and blogpost, and Dr. Logan Schneider, visiting sleep neurologist affiliated with the Stanford/VA Alzheimer’s Center and Stanford Sleep Center, whose clinical expertise and contributions were invaluable to continuously guide this research. In addition to the authors, key contributors to this research from Google Health include Jeffrey Yu, Allen Jiang, Arno Charton, Jake Garrison, Navreet Gill, Sinan Hersek, Yijie Hong, Jonathan Hsu, Andi Janti, Ajay Kannan, Mukil Kesavan, Linda Lei, Kunal Okhandiar‎, Xiaojun Ping, Jo Schaeffer, Neil Smith, Siddhant Swaroop, Bhavana Koka, Anupam Pathak, Dr. Jim Taylor, and the extended team. Another special thanks to Ken Mixter for his support and contributions to the development and integration of this technology into Nest Hub. Thanks to Mark Malhotra and Shwetak Patel for their ongoing leadership, as well as the Nest, Fit, Soli, and Assistant teams we collaborated with to build and validate Sleep Sensing on Nest Hub.


1 Not intended to diagnose, cure, mitigate, prevent or treat any disease or condition. 

Source: Google AI Blog


Contactless Sleep Sensing in Nest Hub

People often turn to technology to manage their health and wellbeing, whether it is to record their daily exercise, measure their heart rate, or increasingly, to understand their sleep patterns. Sleep is foundational to a person’s everyday wellbeing and can be impacted by (and in turn, have an impact on) other aspects of one’s life — mood, energy, diet, productivity, and more.

As part of our ongoing efforts to support people’s health and happiness, today we announced Sleep Sensing in the new Nest Hub, which uses radar-based sleep tracking in addition to an algorithm for cough and snore detection. While not intended for medical purposes1, Sleep Sensing is an opt-in feature that can help users better understand their nighttime wellness using a contactless bedside setup. Here we describe the technologies behind Sleep Sensing and discuss how we leverage on-device signal processing to enable sleep monitoring (comparable to other clinical- and consumer-grade devices) in a way that protects user privacy.

Soli for Sleep Tracking
Sleep Sensing in Nest Hub demonstrates the first wellness application of Soli, a miniature radar sensor that can be used for gesture sensing at various scales, from a finger tap to movements of a person’s body. In Pixel 4, Soli powers Motion Sense, enabling touchless interactions with the phone to skip songs, snooze alarms, and silence phone calls. We extended this technology and developed an embedded Soli-based algorithm that could be implemented in Nest Hub for sleep tracking.

Soli consists of a millimeter-wave frequency-modulated continuous wave (FMCW) radar transceiver that emits an ultra-low power radio wave and measures the reflected signal from the scene of interest. The frequency spectrum of the reflected signal contains an aggregate representation of the distance and velocity of objects within the scene. This signal can be processed to isolate a specified range of interest, such as a user’s sleeping area, and to detect and characterize a wide range of motions within this region, ranging from large body movements to sub-centimeter respiration.

Soli spectrogram illustrating its ability to detect a wide range of motions, characterized as (a) an empty room (no variation in the reflected signal demonstrated by the black space), (b) large pose changes, (c) brief limb movements, and (d) sub-centimeter chest and torso displacements from respiration while at rest.

In order to make use of this signal for Sleep Sensing, it was necessary to design an algorithm that could determine whether a person is present in the specified sleeping area and, if so, whether the person is asleep or awake. We designed a custom machine-learning (ML) model to efficiently process a continuous stream of 3D radar tensors (summarizing activity over a range of distances, frequencies, and time) and automatically classify each feature into one of three possible states: absent, awake, and asleep.

To train and evaluate the model, we recorded more than a million hours of radar data from thousands of individuals, along with thousands of sleep diaries, reference sensor recordings, and external annotations. We then leveraged the TensorFlow Extended framework to construct a training pipeline to process this data and produce an efficient TensorFlow Lite embedded model. In addition, we created an automatic calibration algorithm that runs during setup to configure the part of the scene on which the classifier will focus. This ensures that the algorithm ignores motion from a person on the other side of the bed or from other areas of the room, such as ceiling fans and swaying curtains.

The custom ML model efficiently processes a continuous stream of 3D radar tensors (summarizing activity over a range of distances, frequencies, and time) to automatically compute probabilities for the likelihood of user presence and wakefulness (awake or asleep).

To validate the accuracy of the algorithm, we compared it to the gold-standard of sleep-wake determination, the polysomnogram sleep study, in a cohort of 33 “healthy sleepers” (those without significant sleep issues, like sleep apnea or insomnia) across a broad age range (19-78 years of age). Sleep studies are typically conducted in clinical and research laboratories in order to collect various body signals (brain waves, muscle activity, respiratory and heart rate measurements, body movement and position, and snoring), which can then be interpreted by trained sleep experts to determine stages of sleep and identify relevant events. To account for variability in how different scorers apply the American Academy of Sleep Medicine’s staging and scoring rules, our study used two board-certified sleep technologists to independently annotate each night of sleep and establish a definitive groundtruth.

We compared our Sleep Sensing algorithm’s outputs to the corresponding groundtruth sleep and wake labels for every 30-second epoch of time to compute standard performance metrics (e.g., sensitivity and specificity). While not a true head-to-head comparison, this study’s results can be compared against previously published studies in similar cohorts with comparable methodologies in order to get a rough estimate of performance. In “Sleep-wake detection with a contactless, bedside radar sleep sensing system”, we share the full details of these validation results, demonstrating sleep-wake estimation equivalent to or, in some cases, better than current clinical and consumer sleep tracking devices.

Aggregate performance from previously published accuracies for detection of sleep (sensitivity) and wake (specificity) of a variety of sleep trackers against polysomnography in a variety of different studies, accounting for 3,990 nights in total. While this is not a head-to-head comparison, the performance of Sleep Sensing on Nest Hub in a population of healthy sleepers who simultaneously underwent polysomnography is added to the figure for rough comparison. The size of each circle is a reflection of the number of nights and the inset illustrates the mean±standard deviation for the performance metrics.

Understanding Sleep Quality with Audio Sensing
The Soli-based sleep tracking algorithm described above gives users a convenient and reliable way to see how much sleep they are getting and when sleep disruptions occur. However, to understand and improve their sleep, users also need to understand why their sleep is disrupted. To assist with this, Nest Hub uses its array of sensors to track common sleep disturbances, such as light level changes or uncomfortable room temperature. In addition to these, respiratory events like coughing and snoring are also frequent sources of disturbance, but people are often unaware of these events.

As with other audio-processing applications like speech or music recognition, coughing and snoring exhibit distinctive temporal patterns in the audio frequency spectrum, and with sufficient data an ML model can be trained to reliably recognize these patterns while simultaneously ignoring a wide variety of background noises, from a humming fan to passing cars. The model uses entirely on-device audio processing with privacy-preserving analysis, with no raw audio data sent to Google’s servers. A user can then opt to save the outputs of the processing (sound occurrences, such as the number of coughs and snore minutes) in Google Fit, in order to view personal insights and summaries of their night time wellness over time.

The Nest Hub displays when snoring and coughing may have disturbed a user’s sleep (top) and can track weekly trends (bottom).

To train the model, we assembled a large, hand-labeled dataset, drawing examples from the publicly available AudioSet research dataset as well as hundreds of thousands of additional real-world audio clips contributed by thousands of individuals.

Log-Mel spectrogram inputs comparing cough (left) and snore (right) audio snippets.

When a user opts in to cough and snore tracking on their bedside Nest Hub, the device first uses its Soli-based sleep algorithms to detect when a user goes to bed. Once it detects that a user has fallen asleep, it then activates its on-device sound sensing model and begins processing audio. The model works by continuously extracting spectrogram-like features from the audio input and feeding them through a convolutional neural network classifier in order to estimate the probability that coughing or snoring is happening at a given instant in time. These estimates are analyzed over the course of the night to produce a report of the overall cough count and snoring duration and highlight exactly when these events occurred.

Conclusion
The new Nest Hub, with its underlying Sleep Sensing features, is a first step in empowering users to understand their nighttime wellness using privacy-preserving radar and audio signals. We continue to research additional ways that ambient sensing and the predictive ability of consumer devices could help people better understand their daily health and wellness in a privacy-preserving way.

Acknowledgements
This work involved collaborative efforts from a multidisciplinary team of software engineers, researchers, clinicians, and cross-functional contributors. Special thanks to D. Shin for his significant contributions to this technology and blogpost, and Dr. Logan Schneider, visiting sleep neurologist affiliated with the Stanford/VA Alzheimer’s Center and Stanford Sleep Center, whose clinical expertise and contributions were invaluable to continuously guide this research. In addition to the authors, key contributors to this research from Google Health include Jeffrey Yu, Allen Jiang, Arno Charton, Jake Garrison, Navreet Gill, Sinan Hersek, Yijie Hong, Jonathan Hsu, Andi Janti, Ajay Kannan, Mukil Kesavan, Linda Lei, Kunal Okhandiar‎, Xiaojun Ping, Jo Schaeffer, Neil Smith, Siddhant Swaroop, Bhavana Koka, Anupam Pathak, Dr. Jim Taylor, and the extended team. Another special thanks to Ken Mixter for his support and contributions to the development and integration of this technology into Nest Hub. Thanks to Mark Malhotra and Shwetak Patel for their ongoing leadership, as well as the Nest, Fit, Soli, and Assistant teams we collaborated with to build and validate Sleep Sensing on Nest Hub.


1 Not intended to diagnose, cure, mitigate, prevent or treat any disease or condition. 

Source: Google AI Blog


Haptics with Input: Using Linear Resonant Actuators for Sensing

As wearables and handheld devices decrease in size, haptics become an increasingly vital channel for feedback, be it through silent alerts or a subtle "click" sensation when pressing buttons on a touch screen. Haptic feedback, ubiquitous in nearly all wearable devices and mobile phones, is commonly enabled by a linear resonant actuator (LRA), a small linear motor that leverages resonance to provide a strong haptic signal in a small package. However, the touch and pressure sensing needed to activate the haptic feedback tend to depend on additional, separate hardware which increases the price, size and complexity of the system.

In “Haptics with Input: Back-EMF in Linear Resonant Actuators to Enable Touch, Pressure and Environmental Awareness”, presented at ACM UIST 2020, we demonstrate that widely available LRAs can sense a wide range of external information, such as touch, tap and pressure, in addition to being able to relay information about contact with the skin, objects and surfaces. We achieve this with off-the-shelf LRAs by multiplexing the actuation with short pulses of custom waveforms that are designed to enable sensing using the back-EMF voltage. We demonstrate the potential of this approach to enable expressive discrete buttons and vibrotactile interfaces and show how the approach could bring rich sensing opportunities to integrated haptics modules in mobile devices, increasing sensing capabilities with fewer components. Our technique is potentially compatible with many existing LRA drivers, as they already employ back-EMF sensing for autotuning of the vibration frequency.

Different off-the-shelf LRAs that work using this technique.

Back-EMF Principle in an LRA
Inside the LRA enclosure is a magnet attached to a small mass, both moving freely on a spring. The magnet moves in response to the excitation voltage introduced by the voice coil. The motion of the oscillating mass produces a counter-electromotive force, or back-EMF, which is a voltage proportional to the rate of change of magnetic flux. A greater oscillation speed creates a larger back-EMF voltage, while a stationary mass generates zero back-EMF voltage.

Anatomy of the LRA.

Active Back-EMF for Sensing
Touching or making contact with the LRA during vibration changes the velocity of the interior mass, as energy is dissipated into the contact object. This works well with soft materials that deform under pressure, such as the human body. A finger, for example, absorbs different amounts of energy depending on the contact force as it flattens against the LRA. By driving the LRA with small amounts of energy, we can measure this phenomenon using the back-EMF voltage. Because leveraging the back-EMF behavior for sensing is an active process, the key insight that enabled this work was the design of a custom, off-resonance driver waveform that allows continuous sensing while minimizing vibrations, sound and power consumption.

Touch and pressure sensing on the LRA.

We measure back-EMF from the floating voltage between the two LRA leads, which requires disconnecting the motor driver briefly to avoid disturbances. While the driver is disconnected, the mass is still oscillating inside the LRA, producing an oscillating back-EMF voltage. Because commercial back-EMF sensing LRA drivers do not provide the raw data, we designed a custom circuit that is able to pick up and amplify small back-EMF voltage. We also generated custom drive pulses that minimize vibrations and energy consumption.

Simplified schematic of the LRA driver and the back-EMF measurement circuit for active sensing.
After exciting the LRA with a short drive pulse, the back-EMF voltage fluctuates due to the continued oscillations of the mass on the spring (top, red line). The change in the back-EMF signal when subject to a finger press depends on the pressure applied (middle/bottom, green/blue lines).

Applications
The behavior of the LRAs used in mobile phones is the same, whether they are on a table, on a soft surface, or hand held. This may cause problems, as a vibrating phone could slide off a glass table or emit loud and unnecessary vibrating sounds. Ideally, the LRA on a phone would automatically adjust based on its environment. We demonstrate our approach for sensing using the LRA back-EMF technique by wiring directly to a Pixel 4’s LRA, and then classifying whether the phone is held in hand, placed on a soft surface (foam), or placed on a table.

Sensing phone surroundings.

We also present a prototype that demonstrates how LRAs could be used as combined input/output devices in portable electronics. We attached two LRAs, one on the left and one on the right side of a phone. The buttons provide tap, touch, and pressure sensing. They are also programmed to provide haptic feedback, once the touch is detected.

Pressure-sensitive side buttons.

There are a number of wearable tactile aid devices, such as sleeves, vests, and bracelets. To transmit tactile feedback to the skin with consistent force, the tactor has to apply the right pressure; it can not be too loose or too tight. Currently, the typical way to do so is through manual adjustment, which can be inconsistent and lacks measurable feedback. We show how the LRA back-EMF technique can be used to continuously monitor the fit bracelet device and prompt the user if it's too tight, too loose, or just right.

Fit sensing bracelet.

Evaluating an LRA as a Sensor
The LRA works well as a pressure sensor, because it has a quadratic response to the force magnitude during touch. Our method works for all five off-the-shelf LRA types that we evaluated. Because the typical power consumption is only 4.27 mA, all-day sensing would only reduce the battery life of a Pixel 4 phone from 25 to 24 hours. The power consumption can be greatly reduced by using low-power amplifiers and employing active sensing only when needed, such as when the phone is active and interacting with the user.

Back-EMF voltage changes when pressure is applied with a finger.

The challenge with active sensing is to minimize vibrations, so they are not perceptible when touching and do not produce audible sound. We optimize the active sensing to produce only 2 dB of sound and 0.45 m/s2 peak-to-peak acceleration, which is just barely perceptible by finger and is quiet, in contrast to the regular 8.49 m/s2 .

Future Work and Conclusion
To see the work presented here in action, please see the video below.

In the future, we plan to explore other sensing techniques, perhaps measuring the current could be an alternative approach. Also, using machine learning could potentially improve the sensing and provide more accurate classification of the complex back-EMF patterns. Our method could be developed further to enable closed-loop feedback with the actuator and sensor, which would allow the actuator to provide the same force, regardless of external conditions.

We believe that this work opens up new opportunities for leveraging existing ubiquitous hardware to provide rich interactions and closed-loop feedback haptic actuators.

Acknowledgments
This work was done by Artem Dementyev, Alex Olwal, and Richard Lyon. Thanks to Mathieu Le Goc and Thad Starner for feedback on the paper.

Source: Google AI Blog


Our best Chromecast yet, now with Google TV


Chromecast changed the way we enjoy our favourite movies, TV shows and YouTube videos by making it easy and inexpensive to bring your online entertainment to your TV—a revolutionary idea in 2013. Today, we have more content choices than ever, sprinkled across an ever-expanding variety of apps, which can make it difficult to find what to watch. This inspired us to rethink what simple and easy content discovery on your TV should look like. So today, we're making our biggest leap yet to help you navigate your entertainment choices, bringing together the best of local and global content into one convenient location, with the all-new Chromecast with Google TV. 
Best Chromecast yet 
Chromecast with Google TV has your favourite Chromecast features and now comes with the all-new Google TV entertainment experience. Google TV experience brings together movies, shows and more from across your apps and subscriptions and organises them just for you. We're also bringing our most requested feature—a remote—to Chromecast. 

A new look, inside and out 
The new Chromecast with Google TV comes in a compact and thin design and is packed with the latest technology to give you the best viewing experience. It neatly plugs into your TV's HDMI port and tucks behind your screen. Power it on and you'll be streaming crystal clear video in up to 4K HDR at up to 60 frames per second in no time. With Dolby Vision, you’ll get extraordinary colour, contrast and brightness on your TV. We also support HDMI pass-through of Dolby audio content. 

More power in your hand 
The new Chromecast voice remote is comfortable to hold, easy to use and full of new features. It has a dedicated Google Assistant button that can help you find something to watch, answer everyday questions like “how's the weather?” or play your favourite artist on YouTube Music all with just your voice. And when it's time to cozy up on the couch for movie night, you can control your smart home lights to set the mood or check your front door with Nest Camera to keep tabs on your pizza delivery. We also have dedicated buttons for popular streaming services, YouTube and Netflix, to give you instant access to the content you love. Best of all, you won't have to juggle multiple remotes thanks to our programmable TV controls for power, volume and input. 

TV just for you 
In need of some good movie or TV recommendations? Google TV's For You tab gives you personalised watch suggestions from across your subscriptions organised based on what you like to watch—even your guilty pleasure reality dramas. Google TV’s Watchlist lets you bookmark movies and shows you want to save for later. You can add to your Watchlist from your phone or laptop, and it will be waiting on your TV when you get home. 
Best of all, you'll also have access to thousands of apps and the ability to browse 400,000+ movies and TV shows sorted and optimised for what you like—ask Google Assistant to see results from across your favourite apps, like YouTube, Netflix, Disney+, Stan, 9Now and ABC iview, among others. 

Starting today Chromecast with Google TV is available for pre-order in Australia for $99 in three fun colours to match your decor or personality: Snow, Sunrise and Sky, and will be available from the Google Store as well as other retailers like JB Hi-Fi, Harvey Norman, OfficeWorks, and The Good Guys starting from October 15. Sunrise and Sky will be exclusively available on Google Store. 


Made for music, the new Nest Audio is here

This year, we’ve all spent a lot of time exploring things to do at home. Some of us gardened, and others baked. We tried at-home workouts, or redecorated the house, took up art projects. But one thing that many—maybe all of us—did? Enjoy a lot of music at home. Personally, I have spent so much more time listening to music during quarantine—bossa nova is my go to soundtrack for doing the dishes and Lil Baby has become one of my favourite artists. 
So, in a time when we’re all listening to more music than ever, we’re especially excited to introduce Nest Audio, our latest smart speaker that is made for music lovers. 

A music machine 
Nest Audio is 75 percent louder and has 50 percent stronger bass than the original Google Home—measurements of both devices were taken in an anechoic chamber at maximum volume, on-axis. With a 19mm tweeter for consistent high frequency coverage and clear vocals and a 75mm mid-woofer that really brings the bass, this smart speaker is a music lover’s dream. 
Nest Audio’s sound is full, clear and natural. We completed more than 500 hours of tuning to ensure balanced lows, mids and highs so that nothing is lacking or overbearing. The bass is significant and the vocals have depth, which makes Nest Audio sound great across genres: classical, R&B, pop and more. The custom-designed tweeter allows each musical detail to come through, and we optimised the grill, fabric and materials so that you can enjoy the audio without distortion. 
Our goal was to ensure that Nest Audio stayed faithful to what the artist intended when they were in the recording studio. We minimised the use of compressors to preserve dynamic range, so that the auditory contrast in the original production is preserved—the quiet parts are delicate and subtle, and the loud parts are more dramatic and powerful. 
Nest Audio also adapts to your home. Our Media EQ feature enables Nest Audio to automatically tune itself to whatever you’re listening to: music, podcasts, audiobooks or hearing a response from Google Assistant. And Ambient IQ lets Nest Audio also adjust the volume of Assistant, news, podcasts, and audiobooks based on the background noise in the home, so you can hear the weather forecast over a noisy dishwasher. 

Whole home audio 
If you have a Google Home, Nest Mini or even a Nest Hub, you can easily make Nest Audio the centre of your whole home sound system. In my living room, I’ve connected two Nest Audio speakers as a stereo pair for left and right channel separation. I also have a Nest Hub Max in my kitchen, a Nest Mini in my bedroom and a Nest Hub in the entryway. These devices are grouped so that I can blast the same song on all of them when I have my daily dance party. 
With our stream transfer feature, I can move music from one device to the other with just my voice. Just last month, we launched multi-room control, which allows you to dynamically group multiple cast-enabled Nest devices in real-time. 

An even faster Assistant 
When we launched Nest Mini last year, we embedded a dedicated machine learning chip with up to one TeraOPS of processing power, which let us move some Google Assistant experiences from our data centres directly onto the device. We’ve leveraged the same ML chip in Nest Audio too.
Google Assistant helps you tackle your day, enjoy your entertainment and control compatible smart home brands like Philips Hue, TP-Link and more. In fact, our users have already set up more than 100 million devices to work with Google Assistant. Plus, if you’re a YouTube Music or Spotify Premium subscriber, you can say, “Hey Google, recommend some music” and Google Assistant will offer a variety of choices from artists and genres that you like, and others like them to choose from.

Differentiated by design 
Typically, a bigger speaker equals bigger sound, but Nest Audio has a really slim profile—so it fits anywhere in the home. In order to maximise audio output, we custom-designed quality drivers and housed them in an enclosure that helps it squeeze out every bit of sound possible. 
Nest Audio is available in two colours in Australia: Chalk and Charcoal. Its soft, rounded edges blend in with your home’s decor, and its minimal footprint doesn't take up too much space on your shelf or countertop. 
We’re continuing our commitment to sustainability with Nest Audio. It’s covered in the same sustainable fabric that we first introduced with Nest Mini last year, and the enclosure (meaning the fabric, housing, foot, and a few smaller parts) is made from 70 percent recycled plastic. 

Starting today Nest Audio is available for pre-order in Australia for $149 at the Google Store and other retailers, including JB Hi-Fi, Harvey Norman, and The Good Guys. It will be on-sale from October 15 through these same retailers, as well as Officeworks and Vodafone. 

Pixel 4a (5G) and Pixel 5 pack 5G speeds and so much more

Today, we hosted Launch Night In, a virtual event introducing new products from across Google that will offer a little joy, entertainment and connection for people. These products bring together the best of Google’s hardware, software and AI to deliver helpful experiences built around you. Not only are these products more helpful; they’re more affordable too. 
Our new smartphones, Pixel 4a with 5G and Pixel 5 offer more helpful Google features backed by the power and speeds of 5G.1 From Google’s latest AI and Assistant features, to the biggest ever batteries we’ve put in a Pixel, to industry-leading camera features, Pixel 4a with 5G and Pixel 5 join our much loved Pixel 4a in providing more help at a more helpful price. 

5G speeds at affordable prices 
5G is the latest in mobile technology, bringing fast download and streaming speeds to users around the world. Whether you’re downloading the latest movie2, listening to your favourite music on YouTube Music, catching up on podcasts with Google Podcast or downloading a game Pixel 4a with 5G and Pixel 5 can provide you with fast speeds at a helpful price.1 Starting at just $799 for Pixel 4a with 5G.

New camera, new lenses—same great photos 
Ask any Pixel owner and they’ll tell you: Pixels take great photos. Pixel 4a with 5G and Pixel 5 are no exception. These phones bring Pixel’s industry-leading photography features to the next level. 
  • Better videos with Cinematic Pan: Pixel 4a with 5G and Pixel 5 come with Cinematic Pan, which gives your videos a professional look with ultrasmooth panning that’s inspired by the equipment Hollywood directors use. 
  • Night Sight in Portrait Mode: Night Sight currently gives you the ability to capture amazing low-light photos—and even the Milky Way with astrophotography. Now, these phones bring the power of Night Sight into Portrait Mode to capture beautifully blurred backgrounds in Portraits even in extremely low light. 
Night Sight in Portrait Mode, captured on Pixel 
  • Portrait Light: Portrait Mode on the Pixel 4a with 5G and Pixel 5 lets you capture beautiful portraits that focus on your subject as the background fades into an artful blur. If the lighting isn’t right, your Pixel can drop in extra light to illuminate your subjects
  • Ultrawide lens for ultra awesome shots: With an ultrawide lens alongside the standard rear camera, you’ll be able to capture the whole scene. And thanks to Google’s software magic, the latest Pixels still get our Super Res Zoom. So whether you’re zooming in or zooming out, you get sharp details and breathtaking images. 
Ultrawide, captured on Pixel 
  • New editor in Google Photos: Even after you’ve captured your portrait, Google Photos can help you add studio-quality light to your portraits of people with Portrait Light, in the new, more helpful Google Photos editor
Stay connected and entertained with Duo 
To make it easier and more enjoyable to stay connected to the most important people in your life, the new HD screen sharing in Duo video calls lets you and a friend watch the same video, cheer on sports with a friend and even plan activities – no matter how far apart you are.3 And with features like Duo Family mode, you will be able to keep kids entertained and engaged with new interactive tools, like colouring over backgrounds, while you video chat. 

A smarter way to record and share audio 
Last year, Recorder made audio recording smarter, with real-time transcriptions and the power of search.4 Now, Recorder makes it even easier to share your favourite audio moments. Since Recorder automatically transcribes every recording, now you can use those transcripts to edit the audio too. Just highlight a sentence to crop or remove its corresponding audio. Once you have something you want others to hear—say a quote from an interview or a new song idea—you can generate a video clip to make sharing your audio easier and more visual than ever. 
Editing in Recorder is easy

To improve searching through your transcripts, smart scrolling will automatically mark important words in longer transcripts so you can quickly jump to the sections you’re looking for as you scroll. But most helpful of all? Recorder still works without an internet connection, so you can transcribe, search and edit from anywhere, anytime. 

The biggest Pixel batteries ever 
Pixel 4a with 5G and Pixel 5 also have all-day batteries that can last up to 48 hours with Extreme Battery Saver.5 This mode automatically limits active apps to just the essentials and lets you choose additional apps you want to keep on. 

And now, the specs 
Like all Pixel devices, security and safety are paramount in Pixel 4a with 5G and Pixel 5. Both devices come with our TitanTM M security chip to help keep your on-device data safe and secure, and both phones will get three years of software and security updates. Your Pixel also has built-in safety features like car crash detection6 and Safety Check.7
Plus, Pixel 5 is designed with the environment in mind; we used 100% recycled aluminium in the back housing enclosure to reduce its carbon footprint. You can charge your Pixel 5 wirelessly8 and even use it to wirelessly charge other Qi-certified devices using Battery Share.9 Pixel 5 also doesn’t mind a little water or dust. The metal unibody can handle being submerged in 1.5 metres of fresh water for 30 minutes.10
When you buy the Google phone, you get more from Google. Pixel 5 and Pixel 4a with 5G come with trial subscriptions to Google’s entertainment, security and storage services for new users.11 If you’re a new user you’ll get a YouTube Premium trial for 3 months, 100 GB of storage with Google One for 3 months and 3 months of Google Play Pass and Gold/Silver Status on Play Points. See g.co/pixel/4a5Goffers or g.co/pixel/5offers, as applicable, for more details.11 
In Australia, Pixel 5 will range in two colours, Just Black and Sorta Sage (selected retailers). It will retail for $999 and can be pre-ordered today from Google Store, Telstra, Optus, Vodafone, JB Hi-Fi, Officeworks and Harvey Norman, and will be available starting October 15. Pixel 4a with 5G will retail for $799 and can be pre-ordered today from JB Hi-Fi, Officeworks and Harvey Norman, and will be available from these retailers in addition to Google Store and Telstra in November ranging in Just Black. 


Looking for the Pixel’s that’s right for you? Head to the Google Store now. 

1 Requires a 5G data plan (sold separately). 5G service and roaming not available on all carrier networks or in all areas. Contact carrier for details about current 5G network performance, compatibility, and availability. Phone connects to 5G networks but, 5G service, speed and performance depend on many factors including, but not limited to, carrier network capabilities, device configuration and capabilities, network traffic, location, signal strength and signal obstruction. Actual results may vary. Some features not available in all areas. Data rates may apply. See g.co/pixel/networkinfo for info. 
2 Download speed claims based on testing videos from three streaming platforms. Average download time was less than sixty seconds. File sizes varied between 449MB and 1.3GB. Download speed depends upon many factors, such as file size, content provider and network connection. Testing conducted in an internal 5G network lab and on pre-production hardware in California in July/August 2020. Actual download speeds may be slower. Australian results may vary. 
3 Screen sharing not available on group calls. Requires Wi-Fi or 5G internet connection. Not available on all apps and content. Data rates may apply. 5G service, speed and performance depend on many factors including, but not limited to, carrier network capabilities, device configuration and capabilities, network traffic, location, signal strength, and signal obstruction. 
4 Transcription and search are available in English only. 
5 For “all day”: Maximum battery life based on testing using a mix of talk, data, standby, and use of other features. Testing conducted on two major US carrier networks using Sub-6 GHz non-standalone 5G (ENDC) connectivity. For “Up to 48 hours”: Maximum battery life based on testing using a mix of talk, data, standby, and use of limited other features that are default in Extreme Battery Saver mode (which disables various features including 5G connectivity). Testing conducted on two major US carrier networks. For both claims: Pixel 4a (5G) and Pixel 5 battery testing conducted by a third party in California in mid 2020 on pre-production hardware and software using default settings, except that, for the “up to 48 hour claim” only, Extreme Battery Saver mode was enabled. Battery life depends upon many factors and usage of certain features will decrease battery life. Actual battery life may be lower.
6 Not available in all languages or countries. Car crash detection may not detect all accidents. High-impact activities may trigger calls to emergency services. This feature is dependent upon network connectivity and other factors and may not be reliable for emergency communications or available in all areas. For country and language availability and more information see g.co/pixel/carcrashdetection. 
7 Personal Safety app features are dependent upon network connectivity and other factors and may not be reliable for emergency communications or available in all areas. For more information, see g.co/pixel/personalsafety. 
8 Qi-compatible. Wireless charger sold separately. 
9 Designed to charge Qi-certified devices. Use of Battery Share significantly reduces Pixel battery life. Cases may interfere with charging and will reduce charging speed. Charge speeds may vary. See g.co/pixel/wirelesscharging for more information. 
10 Pixel 5 has a dust and water protection rating of IP68 under IEC standard 60529. Charger and accessories are not water-resistant or dust-resistant. Water and dust resistance are not permanent conditions and may be compromised due to normal wear and tear, repair, disassembly or damage. 
11 The Google One, Google Play Pass, Google Play Points, and YouTube Premium offers are available to eligible new users with the purchase of Pixel 4a (5G) or Pixel 5. Offer expires April 30, 2021 at 11:59pm PT. See g.co/pixel/4a5Goffers or g.co/pixel/5offers, as applicable, for more details.