Tag Archives: Android

Jetpack Compose APIs for building adaptive layouts using Material guidance now stable

Posted by Alex Vanyo – Developer Relations Engineer

The 1.0 stable version of the Compose adaptive APIs with Material guidance is out, ready to be used in production. The library helps you build adaptive layouts that provide an optimized user experience on any window size.

The team at SAP Mobile Start were early adopters of the Compose adaptive APIs. It took their developers only five minutes to integrate the NavigationSuiteScaffold from the new Compose Material 3 adaptive library, rapidly adapting the app’s navigation UI to different window sizes.

Each of the new components in the library, NavigationSuiteScaffold, ListDetailPaneScaffold and SupportingPaneScaffold are adaptive: based on the window size and posture, different components are displayed to the user based on which one is most appropriate in the current context. This helps build UI that adapts to a wide variety of window sizes instead of just stretching layouts.

For an overview of the components, check out the dedicated I/O session and our new documentation pages to get started.

In this post, we’re going to take a more detailed look at the layering of the new library so you have a better understanding of how customisable it is, to fit a wide variety of use cases you might have.

Similar to Compose itself, the adaptive libraries are layered into multiple dependencies, so that you can choose the appropriate level of abstraction for your application.There are four new artifacts as part of the adaptive libraries:

    • For the core building blocks for building adaptive UI, including computing the window size class and the current posture, add androidx.compose.material3.adaptive:adaptive:1.0.0

    • For implementing multi-pane layouts, add androidx.compose.material3.adaptive:adaptive-layout:1.0.0


    • For standalone navigators for the multi-pane scaffold layouts, add androidx.compose.material3.adaptive:adaptive-navigation:1.0.0

    • For implementing adaptive navigation UI, add androidx.compose.material3:material3-adaptive-navigation-suite:1.3.0

The libraries have the following dependencies:

Flow diagram showing dependencies between material3-adaptive 1.0.0 and material 1.3.0 libraries
New library dependency graph

To explore this layering more, let’s start with the highest level example with the most built-in functionality using a NavigableListDetailPaneScaffold from androidx.compose.material3.adaptive:adaptive-navigation:

val navigator = rememberListDetailPaneScaffoldNavigator<Any>()

NavigableListDetailPaneScaffold(
    navigator = navigator,
    listPane = {
        // List pane
    },
    detailPane = {
        // Detail pane
    },
)

This snippet of code gives you all of our recommended adaptive behavior out of the box for a list-detail layout: determining how many panes to show based on the current window size, hiding and showing the correct pane when the window size changes depending on the previous state of the UI, and having the back button conditionally bring the user back to the list, depending on the window size and the current state.

A list layout adapting to and from a list detail layout depending on the window size

This encapsulates a lot of behavior – and this might be all you need, and you don’t need to go any deeper!

However, there may be reasons why you may want to tweak this behavior, or more directly manage the state by hoisting parts of it in a different way.

Remember, each layer builds upon the last. This snippet is at the outermost layer, and we can start unwrapping the layers to customize it where we need.

Let’s go one level deeper with NavigableListDetailPaneScaffold and drop down one layer. Behavior won’t change at all with these direct inlinings, since we are just inlining the default behavior at each step:

(Fun fact: You can follow along with this directly in Android Studio and for any other component you desire. If you choose Refactor > Inline function, you can directly replace a component with its implementation. You can’t delete the original function in the library of course.)

val navigator = rememberListDetailPaneScaffoldNavigator<Any>()

BackHandler(
    enabled = navigator.canNavigateBack(BackNavigationBehavior.PopUntilContentChange)
) {
    navigator.navigateBack(BackNavigationBehavior.PopUntilContentChange)
}
ListDetailPaneScaffold(
    directive = navigator.scaffoldDirective,
    value = navigator.scaffoldValue,
    listPane = {
        // List pane
    },
    detailPane = {
        // Detail pane
    },
)

With the first inlining, we see the BackHandler that NavigableListDetailPaneScaffold includes by default. If using ListDetailPaneScaffold directly, back handling is left up to the developer to include and hoist to the appropriate place.

This also reveals how the navigator provides two pieces of state to control the ListDetailPaneScaffold:

    • directive —- how the panes should be arranged in the ListDetailPaneScaffold, and
    • value —- the current state of the panes, as calculated from the directive and the current navigation state.

These are both controlled by the navigator, and the next unpeeling shows us the default arguments to the navigator for directive and the adapt strategy, which is used to calculate value:

val navigator = rememberListDetailPaneScaffoldNavigator<Any>(
    scaffoldDirective = calculatePaneScaffoldDirective(currentWindowAdaptiveInfo()),
    adaptStrategies = ListDetailPaneScaffoldDefaults.adaptStrategies(),
)

BackHandler(
    enabled = navigator.canNavigateBack(BackNavigationBehavior.PopUntilContentChange)
) {
    navigator.navigateBack(BackNavigationBehavior.PopUntilContentChange)
}
ListDetailPaneScaffold(
    directive = navigator.scaffoldDirective,
    value = navigator.scaffoldValue,
    listPane = {
        // List pane
    },
    detailPane = {
        // Detail pane
    },
)

The directive controls the behavior for how many panes to show and the pane spacing, based on currentWindowAdaptiveInfo, which contains the size and posture of the window.

This can be customized with a different directive, to show two panes side-by-side at a smaller medium width:

val navigator = rememberListDetailPaneScaffoldNavigator<Any>(
    scaffoldDirective = calculatePaneScaffoldDirectiveWithTwoPanesOnMediumWidth(currentWindowAdaptiveInfo()),
    adaptStrategies = ListDetailPaneScaffoldDefaults.adaptStrategies(),
)

By default, showing two panes at a medium width can result in UI that is too narrow, especially for complex content. However, this can be a good option to use the window space more optimally by showing two panes for less complex content.

The AdaptStrategy controls what happens to panes when there isn’t enough space to show all of them. Right now, this always hides panes for which there isn’t enough space.

This directive is used by the navigator to drive its logic and, combined with the adapt strategy to determine the scaffold value, the resulting target state for each of the panes.

The scaffold directive and the scaffold value are then passed to the ListDetailPaneScaffold, driving the behavior of the scaffold.

This layering allows hoisting the scaffold state away from the display of the scaffold itself. This layering also allows custom implementations for controlling how the scaffold works and for hoisting related state. For example, if you are using a custom navigation solution instead of the navigator, you could drive the ListDetailPaneScaffold directly with state derived from your custom navigation solution.

The layering is enforced in the library with the different artifacts:

    • androidx.compose.material3.adaptive:adaptive contains the underlying methods to calculate the current window adaptive info
    • androidx.compose.material3.adaptive:adaptive-layout contains the layouts ListDetailPaneScaffold and SupportingPaneScaffold
    • androidx.compose.material3.adaptive:adaptive-navigation contains the navigator APIs (like rememberListDetailPaneScaffoldNavigator)

Therefore, if you aren’t going to use the navigator and instead use a custom navigation solution, you can skip using androidx.compose.material3.adaptive:adaptive-navigation and depend on androidx.compose.material3.adaptive:adaptive-layout directly.

When adding the Compose Adaptive library to your app, start with the most fully featured layer, and then unwrap if needed to tweak behavior. As we continue to work on the library and add new features, we’ll keep adding them to the appropriate layer. Using the higher-level layers will mean that you will be able to get these new features most easily. If you need to, you can use lower layers to get more fine-grained control, but that also means that more responsibility for behavior is transferred to your app, just like the layering in Compose itself.

Try out the new components today, and send us your feedback for bugs and feature requests.

SAP integrated NavigationSuiteScaffold in just 5 minutes to create adaptive navigation UI

Posted by Alex Vanyo – Developer Relations Engineer

SAP Mobile Start is an app that centralizes access to SAP's mobile business suite, a hub for users to keep track of their companies’ processes and data so they can efficiently manage their daily to-dos while on the move.

Recently, SAP Mobile Start developers prioritized building an adaptive app that looks great across devices, including tablets and foldables, to create a more seamless user experience. Using Jetpack Compose and Material 3 design, the team efficiently implemented intuitive, user-friendly features to increase accessibility across its users’ preferred devices.


Adaptive design across devices

With over 300 million daily active users on foldables, tablets, and Chromebooks today, building apps that adapt to varied screen sizes is important for providing an optimal user experience. But simply stretching the UI to fit different screen sizes can drastically alter it from its original form, obscuring the interface and impairing the user experience.

“We focused on situations where we could make better use of available space on large screens,” said Laura Bergmann, UX designer for SAP. “We wanted to get rid of screens that are stretched from edge to edge, full-screen drill-downs or dialogs, and use space more efficiently.”

Now, after optimizing for different devices, SAP Mobile Start dynamically adjusts its layouts by swapping components and showing or hiding content based on the available window size instead of stretching UI elements to match a device's screen.

The SAP team also implemented canonical layouts, common UI designs that split a screen into panes according to its size. By separating content into panes, SAP’s users can manage their business workflows more productively. Depending on the window size class, the supporting pane adjusts the UI without additional custom logic. For example, compact windows typically utilize one pane, while larger windows can utilize multiple.

“Adopting the new canonical layouts from Google helped us focus more on designing unique app capabilities for SAP’s business scenarios,” said Laura. “With the available navigational elements and patterns, we can now channel our efforts into creating a more engaging user experience without reinventing the wheel.”

SAP developers started by implementing supporting panes to create multi-pane layouts that efficiently utilize on-screen space. The first place developers added supporting panes was on the app’s “To-Do” details page. To-dos used to be managed in a single pane, making it difficult to review the comments and tickets simultaneously. Now, tickets and comments are reviewed in primary and secondary panes on the same screen using SupportingPaneScaffold.

We focused on making better use of the available space in large screens. We wanted to move away from UIs that are stretched to adaptive layouts that enhance productivity.”  — Laura Bergmann, UX designer at SAP

Fast implementation using Compose Material 3 Adaptive library

SAP Mobile Start is built entirely with Jetpack Compose, Android’s modern declarative toolkit for building native UI. Compose helped SAP developers build new UI faster and easier than ever before thanks to composables, reusable code blocks for building common UI components. The team also used Compose Navigation to integrate seamless navigation between composables, optimizing travel between new UI on all screens.

It took developers only five minutes to integrate the NavigationSuiteScaffold from the new Compose Material 3 adaptive library, rapidly adapting the app’s navigation UI to different window sizes, switching between a bottom navigation bar and a vertical navigation rail. It also eliminated the need for custom logic, which previously determined the navigation component based on various window size classes. The NavigationSuiteScaffold also reduced the custom navigation UI logic code by 59%, from 379 lines to 156.

“Jetpack Compose simplified UI development,” said Aditya Arora, lead Android developer. “Its declarative nature, coupled with built-in support for Material Design and dark theme, significantly increased our development efficiency. By simply describing the desired UI, we've reduced code complexity and improved maintainability.”

SAP developers used live edit and layout inspector in Android Studio to test and optimize the app for large screens. These features were “total game changers” for the SAP team because they helped iterate and inspect layout issues faster when optimizing for new screens.

With its @PreviewScreenSizes annotation and device streaming powered by Firebase, Jetpack Compose also made testing the app's UI across various screen sizes easier. SAP developers look forward to Compose Screenshot Testing being completed, which will further streamline UI testing and ensure greater visual consistency within the app.

Using Jetpack Compose, SAP developers also quickly and easily implemented new Material 3 design concepts from the Compose M3 Adaptive library. Material 3 design emphasizes personalizing the app experience, improving interactions with modern visual aesthetics.

Compose's flexibility made replacing the standard Material Theme with their own custom Fiori Horizon Theme simple, ensuring a consistent visual appearance across SAP apps. “As early adopters of the Compose M3 Adaptive library, we collaborated with Google to refine the API,” said Aditya. “Since our app is completely Compose-based, leveraging the new Compose Material 3 Adaptive library was a piece of cake.”

A list layout adapting to and from a list detail layout depending on the window size

As large-screen devices like tablets, foldables, and Chromebooks become more popular, building layouts that adapt to varied screen sizes becomes increasingly crucial. For SAP Mobile Start developers, reimagining their app across devices using Jetpack Compose and Material 3 design guidelines was simple. Using Android’s collection of tools and resources, creating adaptive UIs for all the new form factors hitting the market today is faster and easier than ever.

“Optimizing for large screens is crucial. The market for tablets, foldables, and Chromebooks is booming. Don't miss out on this opportunity to improve your user experience and expand your app's reach,” said Aditya.

Get started

Learn how to improve your UX by optimizing for large screens and foldables using Jetpack Compose and Material 3 design.

Google Workspace Updates Weekly Recap – September 6, 2024

1 New update

Unless otherwise indicated, the features below are available to all Google Workspace customers, and are fully launched or in the process of rolling out. Rollouts should take no more than 15 business days to complete if launching to both Rapid and Scheduled Release at the same time. If not, each stage of rollout should take no more than 15 business days to complete.




Improved user experience for Google Meet on Android devices
If you’re joining a Google Meet call from Android phone, tablets or large screen devices, you’ll now see a more streamlined, space-efficient experience with edge-to-edge video. We’ve expanded the video feed to encompass spaces where there were previously margins around the video feed. This helps provide a richer, more immersive viewing experience. You’ll also notice a sleeker user interface for meeting controls, and clearer indicators for information such as the meeting title. | Rollout to Rapid Release and Scheduled Release domains is complete. | Available now for all Google Workspace customers, Workspace Individual Subscribers, and users with personal Google accounts. | Visit the Help Center to learn more about joining a meeting.

mobile experience - Improved user experience for Google Meet on Android devices
tablet - Improved user experience for Google Meet on Android devices




Previous announcements

The announcements below were published on the Workspace Updates blog earlier this week. Please refer to the original blog posts for complete details.


Gemini (gemini.google.com) now shows related content links in its responses 
You can now access additional information on topics directly in Gemini’s (gemini.google.com) responses to your prompts. Specifically, you’ll see links to related content in responses to fact-seeking prompts — you can click the arrow chips to dive deeper into the topic. If you have a Gemini for Workspace license and Google Workspace extensions in Gemini are enabled, Gemini will also now include inline links to relevant emails referenced in responses where the Gmail extension is used. | Learn more about related content links shown in Gemini.

View your most relevant Google Drive folders and files on a single page 
You will now see a combined, unified view for file and folder suggestions on the Drive homepage that leverages machine learning to help you find and organize your most relevant content faster and intuitively. | Learn more about the view in Drive.

Empowering Google Workspace customers to take control of their emissions with Electricity Maps
To help our customers continue to understand and measure the carbon intensity of their cloud computing, we have partnered with Electricity Maps to provide hourly emissions data within the Carbon Footprint report. | Learn more about Electricity Maps. 


Completed rollouts

The features below completed their rollouts to Rapid Release domains, Scheduled Release domains, or both. Please refer to the original blog posts for additional details.


Scheduled Release Domains: 
Rapid and Scheduled Release Domains: 

For a recap of announcements in the past six months, check out What’s new in Google Workspace (recent releases).  

Deploying Rust in Existing Firmware Codebases

Android's use of safe-by-design principles drives our adoption of memory-safe languages like Rust, making exploitation of the OS increasingly difficult with every release. To provide a secure foundation, we’re extending hardening and the use of memory-safe languages to low-level firmware (including in Trusty apps).


In this blog post, we'll show you how to gradually introduce Rust into your existing firmware, prioritizing new code and the most security-critical code. You'll see how easy it is to boost security with drop-in Rust replacements, and we'll even demonstrate how the Rust toolchain can handle specialized bare-metal targets.


Drop-in Rust replacements for C code are not a novel idea and have been used in other cases, such as librsvg’s adoption of Rust which involved replacing C functions with Rust functions in-place. We seek to demonstrate that this approach is viable for firmware, providing a path to memory-safety in an efficient and effective manner.

Memory Safety for Firmware

Firmware serves as the interface between hardware and higher-level software. Due to the lack of software security mechanisms that are standard in higher-level software, vulnerabilities in firmware code can be dangerously exploited by malicious actors. Modern phones contain many coprocessors responsible for handling various operations, and each of these run their own firmware. Often, firmware consists of large legacy code bases written in memory-unsafe languages such as C or C++. Memory unsafety is the leading cause of vulnerabilities in Android, Chrome, and many other code bases.


Rust provides a memory-safe alternative to C and C++ with comparable performance and code size. Additionally it supports interoperability with C with no overhead. The Android team has discussed Rust for bare-metal firmware previously, and has developed training specifically for this domain.

Incremental Rust Adoption

Our incremental approach focusing on replacing new and highest risk existing code (for example, code which processes external untrusted input) can provide maximum security benefits with the least amount of effort. Simply writing any new code in Rust reduces the number of new vulnerabilities and over time can lead to a reduction in the number of outstanding vulnerabilities.


You can replace existing C functionality by writing a thin Rust shim that translates between an existing Rust API and the C API the codebase expects. The C API is replicated and exported by the shim for the existing codebase to link against. The shim serves as a wrapper around the Rust library API, bridging the existing C API and the Rust API. This is a common approach when rewriting or replacing existing libraries with a Rust alternative.

Challenges and Considerations

There are several challenges you need to consider before introducing Rust to your firmware codebase. In the following section we address the general state of no_std Rust (that is, bare-metal Rust code), how to find the right off-the-shelf crate (a rust library), porting an std crate to no_std, using Bindgen to produce FFI bindings, how to approach allocators and panics, and how to set up your toolchain.

The Rust Standard Library and Bare-Metal Environments

Rust's standard library consists of three crates: core, alloc, and std. The core crate is always available. The alloc crate requires an allocator for its functionality. The std crate assumes a full-blown operating system and is commonly not supported in bare-metal environments. A third-party crate indicates it doesn’t rely on std through the crate-level #![no_std] attribute. This crate is said to be no_std compatible. The rest of the blog will focus on these.

Choosing a Component to Replace

When choosing a component to replace, focus on self-contained components with robust testing. Ideally, the components functionality can be provided by an open-source implementation readily available which supports bare-metal environments.


Parsers which handle standard and commonly used data formats or protocols (such as, XML or DNS) are good initial candidates. This ensures the initial effort focuses on the challenges of integrating Rust with the existing code base and build system rather than the particulars of a complex component and simplifies testing. This approach eases introducing more Rust later on.

Choosing a Pre-Existing Crate (Rust Library)

Picking the right open-source crate (Rust library) to replace the chosen component is crucial. Things to consider are:

  • Is the crate well maintained, for example, are open issues being addressed and does it use recent crate versions?

  • How widely used is the crate? This may be used as a quality signal, but also important to consider in the context of using crates later on which may depend on it.

  • Does the crate have acceptable documentation?

  • Does it have acceptable test coverage?


Additionally, the crate should ideally be no_std compatible, meaning the standard library is either unused or can be disabled. While a wide range of no_std compatible crates exist, others do not yet support this mode of operation – in those cases, see the next section on converting a std library to no_std.


By convention, crates which optionally support no_std will provide an std feature to indicate whether the standard library should be used. Similarly, the alloc feature usually indicates using an allocator is optional.


Note: Even when a library declares #![no_std] in its source, there is no guarantee that its dependencies don’t depend on std. We recommend looking through the dependency tree to ensure that all dependencies support no_std, or test whether the library compiles for a no_std target. The only way to know is currently by trying to compile the crate for a bare-metal target.



For example, one approach is to run cargo check with a bare-metal toolchain provided through rustup:

$ rustup target add aarch64-unknown-none

$ cargo check --target aarch64-unknown-none --no-default-features


Porting a std Library to no_std

If a library does not support no_std, it might still be possible to port it to a bare-metal environment – especially file format parsers and other OS agnostic workloads. Higher-level functionality such as file handling, threading, and async code may present more of a challenge. In those cases, such functionality can be hidden behind feature flags to still provide the core functionality in a no_std build.

To port a std crate to no_std (core+alloc):

  • In the cargo.toml file, add a std feature, then add this std feature to the default features

  • Add the following lines to the top of the lib.rs:

#![no_std]


#[cfg(feature = "std")]

extern crate std;

extern crate alloc;

Then, iteratively fix all occurring compiler errors as follows:

  1. Move any use directives from std to either core or alloc.

  2. Add use directives for all types that would otherwise automatically be imported by the std prelude, such as alloc::vec::Vec and alloc::string::String.

  3. Hide anything that doesn't exist in core or alloc and cannot otherwise be supported in the no_std build (such as file system accesses) behind a #[cfg(feature = "std")] guard.

  4. Anything that needs to interact with the embedded environment may need to be explicitly handled, such as functions for I/O. These likely need to be behind a #[cfg(not(feature = "std"))] guard.

  5. Disable std for all dependencies (that is, change their definitions in Cargo.toml, if using Cargo).

This needs to be repeated for all dependencies within the crate dependency tree that do not support no_std yet.

Custom Target Architectures

There are a number of officially supported targets by the Rust compiler, however, many bare-metal targets are missing from that list. Thankfully, the Rust compiler lowers to LLVM IR and uses an internal copy of LLVM to lower to machine code. Thus, it can support any target architecture that LLVM supports by defining a custom target.


Defining a custom target requires a toolchain built with the channel set to dev or nightly. Rust’s Embedonomicon has a wealth of information on this subject and should be referred to as the source of truth. 


To give a quick overview, a custom target JSON file can be constructed by finding a similar supported target and dumping the JSON representation:


$ rustc --print target-list

[...]

armv7a-none-eabi

[...]


$ rustc -Z unstable-options --print target-spec-json --target armv7a-none-eabi


This will print out a target JSON that looks something like:

$ rustc --print target-spec-json -Z unstable-options --target=armv7a-none-eabi

{

  "abi": "eabi",

  "arch": "arm",

  "c-enum-min-bits": 8,

  "crt-objects-fallback": "false",

  "data-layout": "e-m:e-p:32:32-Fi8-i64:64-v128:64:128-a:0:32-n32-S64",

  [...]

}


This output can provide a starting point for defining your target. Of particular note, the data-layout field is defined in the LLVM documentation.


Once the target is defined, libcore and liballoc (and libstd, if applicable) must be built from source for the newly defined target. If using Cargo, building with -Z build-std accomplishes this, indicating that these libraries should be built from source for your target along with your crate module:

# set build-std to the list of libraries needed

cargo build -Z build-std=core,alloc --target my_target.json

Building Rust With LLVM Prebuilts

If the bare-metal architecture is not supported by the LLVM bundled internal to the Rust toolchain, a custom Rust toolchain can be produced with any LLVM prebuilts that support the target.


The instructions for building a Rust toolchain can be found in detail in the Rust Compiler Developer Guide. In the config.toml, llvm-config must be set to the path of the LLVM prebuilts.


You can find the latest Rust Toolchain supported by a particular version of LLVM by checking the release notes and looking for releases which bump up the minimum supported LLVM version. For example, Rust 1.76 bumped the minimum LLVM to 16 and 1.73 bumped the minimum LLVM to 15. That means with LLVM15 prebuilts, the latest Rust toolchain that can be built is 1.75.

Creating a Drop-In Rust Shim

To create a drop-in replacement for the C/C++ function or API being replaced, the shim needs two things: it must provide the same API as the replaced library and it must know how to run in the firmware’s bare-metal environment.

Exposing the Same API

The first is achieved by defining a Rust FFI interface with the same function signatures.


We try to keep the amount of unsafe Rust as minimal as possible by putting the actual implementation in a safe function and exposing a thin wrapper type around.


For example, the FreeRTOS coreJSON example includes a JSON_Validate C function with the following signature:

JSONStatus_t JSON_Validate( const char * buf, size_t max );


We can write a shim in Rust between it and the memory safe serde_json crate to expose the C function signature. We try to keep the unsafe code to a minimum and call through to a safe function early:

#[no_mangle]

pub unsafe extern "C" fn JSON_Validate(buf: *const c_char, len: usize) -> JSONStatus_t {

    if buf.is_null() {

        JSONStatus::JSONNullParameter as _

    } else if len == 0 {

        JSONStatus::JSONBadParameter as _

    } else {

        json_validate(slice_from_raw_parts(buf as _, len).as_ref().unwrap()) as _

    }

}


// No more unsafe code in here.

fn json_validate(buf: &[u8]) -> JSONStatus {

    if serde_json::from_slice::<Value>(buf).is_ok() {

        JSONStatus::JSONSuccess

    } else {

        ILLEGAL_DOC

    }

}



Note: This is a very simple example. For a highly resource constrained target, you can avoid alloc and use serde_json_core, which has even lower overhead but requires pre-defining the JSON structure so it can be allocated on the stack.



For further details on how to create an FFI interface, the Rustinomicon covers this topic extensively.

Calling Back to C/C++ Code

In order for any Rust component to be functional within a C-based firmware, it will need to call back into the C code for things such as allocations or logging. Thankfully, there are a variety of tools available which automatically generate Rust FFI bindings to C. That way, C functions can easily be invoked from Rust.


The standard means of doing this is with the Bindgen tool. You can use Bindgen to parse all relevant C headers that define the functions Rust needs to call into. It's important to invoke Bindgen with the same CFLAGS as the code in question is built with, to ensure that the bindings are generated correctly.


Experimental support for producing bindings to static inline functions is also available.

Hooking Up The Firmware’s Bare-Metal Environment

Next we need to hook up Rust panic handlers, global allocators, and critical section handlers to the existing code base. This requires producing definitions for each of these which call into the existing firmware C functions.


The Rust panic handler must be defined to handle unexpected states or failed assertions. A custom panic handler can be defined via the panic_handler attribute. This is specific to the target and should, in most cases, either point to an abort function for the current task/process, or a panic function provided by the environment.


If an allocator is available in the firmware and the crate relies on the alloc crate, the Rust allocator can be hooked up by defining a global allocator implementing GlobalAlloc.


If the crate in question relies on concurrency, critical sections will need to be handled. Rust's core or alloc crates do not directly provide a means for defining this, however the critical_section crate is commonly used to handle this functionality for a number of architectures, and can be extended to support more.


It can be useful to hook up functions for logging as well. Simple wrappers around the firmware’s existing logging functions can expose these to Rust and be used in place of print or eprint and the like. A convenient option is to implement the Log trait.

Fallible Allocations and alloc

Rusts alloc crate normally assumes that allocations are infallible (that is, memory allocations won’t fail). However due to memory constraints this isn’t true in most bare-metal environments. Under normal circumstances Rust panics and/or aborts when an allocation fails; this may be acceptable behavior for some bare-metal environments, in which case there are no further considerations when using alloc.


If there’s a clear justification or requirement for fallible allocations however, additional effort is required to ensure that either allocations can’t fail or that failures are handled. 


One approach is to use a crate that provides statically allocated fallible collections, such as the heapless crate, or dynamic fallible allocations like fallible_vec. Another is to exclusively use try_* methods such as Vec::try_reserve, which check if the allocation is possible.


Rust is in the process of formalizing better support for fallible allocations, with an experimental allocator in nightly allowing failed allocations to be handled by the implementation. There is also the unstable cfg flag for alloc called no_global_oom_handling which removes the infallible methods, ensuring they are not used.

Build Optimizations

Building the Rust library with LTO is necessary to optimize for code size. The existing C/C++ code base does not need to be built with LTO when passing -C lto=true to rustc. Additionally, setting -C codegen-unit=1 results in further optimizations in addition to reproducibility. 


If using Cargo to build, the following Cargo.toml settings are recommended to reduce the output library size:


[profile.release]

panic = "abort"

lto = true

codegen-units = 1

strip = "symbols"


# opt-level "z" may produce better results in some circumstances

opt-level = "s" 


Passing the -Z remap-cwd-prefix=. flag to rustc or to Cargo via the RUSTFLAGS env var when building with Cargo to strip cwd path strings.


In terms of performance, Rust demonstrates similar performance to C. The most relevant example may be the Rust binder Linux kernel driver, which found “that Rust binder has similar performance to C binder”.


When linking LTO’d Rust staticlibs together with C/C++, it’s recommended to ensure a single Rust staticlib ends up in the final linkage, otherwise there may be duplicate symbol errors when linking. This may mean combining multiple Rust shims into a single static library by re-exporting them from a wrapper module.

Memory Safety for Firmware, Today

Using the process outlined in this blog post, You can begin to introduce Rust into large legacy firmware code bases immediately. Replacing security critical components with off-the-shelf open-source memory-safe implementations and developing new features in a memory safe language will lead to fewer critical vulnerabilities while also providing an improved developer experience.


Special thanks to our colleagues who have supported and contributed to these efforts: Roger Piqueras Jover, Stephan Chen, Gil Cukierman, Andrew Walbran, and Erik Gilling