XLS: Accelerated HW Synthesis

Docs | Quick Start | Tutorials

What is XLS?

XLS implements a High Level Synthesis toolchain that produces synthesizable designs (Verilog and SystemVerilog) from flexible, high-level descriptions of functionality. It is Apache 2 licensed.

XLS (Accelerated HW Synthesis) aims to be the Software Development Kit (SDK) for the End of Moore's Law (EoML) era. In this "age of specialization", software and hardware engineers must do more co-design across their domain boundaries -- collaborate on shared artifacts, understand each other's cost models, and share tooling/methodology. XLS attempts to leverage automation, software engineers, and machine cycles to accelerate this overall process.

XLS enables the rapid development of hardware IP that also runs as efficient host software via "software style" methodology. An XLS design runs at native speeds for use in host software or a simulator, but that design can also generate hardware block output -- the XLS tools' correctness ensures (and provides tools to help formally verify) that they are functionally identical.

XLS supports both (optionally pipelined) functions with pure-wire I/O interfaces and concurrent processes (or procs). Procs are stateful, allowing induction over time, and include more general communication interfaces.

State of the Project

XLS is experimental, undergoing rapid development, and not an officially supported Google product. Expect bugs and sharp edges. Please help by trying it out, running through some tutorials, reporting bugs.

We are early stage and this has some practical effects:

We welcome your issues and PRs.
- Please try to lead with an issue. Engage us in conversation if you wish to
  upstream changes. Sending a PR without back and forth with us in an issue
  may be a longer road to success. If you believe your PR is ready and has
  not received a response within two business days, please ping the issue
  with what you think are next steps.
At the current point in its evolution, we regularly improve DSLX without
considering backward compatibility.
- If you are building a corpus of hardware with XLS, please be thoughtful
  about your process for bringing in new versions of the compiler.

Colab Notebooks

For a more setup-free and environment-independent way of trying out XLS, see our colab notebooks:

bit.ly/learn-xls: a "learn XLS in Y minutes" style walkthrough in DSLX, our Rust-inspired domain specific language (DSL).
bit.ly/xls-playground: an XLS evaluation environment that can run the following interactively:
- XLS tests
- XLS→IR conversion
- IR→Verilog codegen
- Verilog synthesis via Yosys (using open PDKs ASAP7 and SKY130)
- Place-and-Route (P&R) via OpenROAD
- Power/Performance/Area (PPA) metric collection

Install Latest Release

The following downloads the latest github repo release binaries for an x64 Linux machine:

# Determine the url of the latest release tarball.
LATEST_XLS_RELEASE_TARBALL_URL=$(curl -s -L \
  -H "Accept: application/vnd.github+json" \
  -H "X-GitHub-Api-Version: 2022-11-28" \
  https://api.github.com/repos/google/xls/releases | \
  grep -m 1 -o 'https://.*/releases/download/.*\.tar\.gz')

# Download the tarball and unpack it, observe the version numbers for each of the included tools.
curl -O -L ${LATEST_XLS_RELEASE_TARBALL_URL}
tar -xzvvf xls-*.tar.gz
cd xls-*/
./interpreter_main --version
./ir_converter_main --version
./opt_main --version
./codegen_main --version
./proto_to_dslx_main --version

Building From Source

Aside from the binary releases (available for x64 Linux as described above), and the available colab notebooks, XLS must be built from source using the Bazel build system.

The following instructions are for the Ubuntu 22.04 (Jammy Jellyfish) Linux distribution.

On an average 8-core VM:

A full initial build without the C++ front-end (e.g. "DSLX only") may take
about 2 hours,
Including the C++ front-end may take up to 6 hours.

Please see the two corresponding command lines below -- we start by assuming Bazel has been installed:

~$ git clone https://github.com/google/xls.git
~$ cd xls

~/xls$ # Follow the bazel install instructions to install bazel 7
~/xls$ # https://bazel.build/install/ubuntu

~/xls$ # Note we're going to tell Ubuntu that `/usr/bin/env python` is actually python3
~/xls$ # here, since that has not been the case by default on past Ubuntus.
~/xls$ # This is important. Without this step, you may experience cryptic error messages:
~/xls$ sudo apt install python3-distutils python3-dev libtinfo5 python-is-python3

~/xls$ # Now build/test in optimized build mode.
~/xls$ # If you don't plan on using the C++ front-end, which is not strictly
~/xls$ # needed (i.e. DSLX front-end only), use this command line:
~/xls$ bazel test -c opt -- //xls/... -//xls/contrib/xlscc/...

~/xls$ # To build everything, including the C++ front-end:
~/xls$ bazel test -c opt -- //xls/...

Reference build/test environment setups are also provided via Dockerfiles, if you have difficulty setting up the (limited set of) dependencies shown above in your environment:

~$ git clone https://github.com/google/xls.git
~$ cd xls
~/xls$ docker build . -f Dockerfile-ubuntu-22.04  # Performs optimized build-and-test.

Adding Additional Build Caching

Many programmers are used to using programs like ccache to improve caching for a build, but Bazel actually ships with very-high quality caching layers. In particular, incremental builds are more safe.

However, there are circumstances where Bazel might decide to recompile files where the results could have been cached locally - or where it might be safe to reuse certain intermediate results, even after a bazel clean. To improve this, you can tell Bazel to use a shared "disk cache", storing files persistently elsewhere on disk; just create a directory somewhere (e.g., ~/.bazel_disk_cache/), and then run:

echo "build --disk_cache=$(realpath ~/.bazel_disk_cache)" >> ~/.bazelrc
echo "test --disk_cache=$(realpath ~/.bazel_disk_cache)" >> ~/.bazelrc

!!! WARNING Bazel does not automate garbage collection of this directory, so it will grow over time without bounds. You will need to clean it up periodically, either manually or with an automated script.

Alternatively, you can add a remote cache that takes care of garbage collection for you. This can be hosted on a personal server or even on the local machine. We've personally had good results with localhost instances of bazel-remote.

Getting Clangd completions

A compile_commands.json file compatible with clangd and similar tools can be created by running xls/dev_tools/make-compilation-db.sh. Follow directions for your editor to install clangd code completion.

Stack Diagram and Project Layout

Navigating a new code base can be daunting; the following description provides a high-level view of the important directories and their intended organization / purpose, and correspond to the components in this XLS stack diagram:

dependency_support: Configuration files that load, build, and expose Bazel targets for external dependencies of XLS.
docs: Generated documentation served via GitHub pages: https://google.github.io/xls/
docs_src: Markdown file sources, rendered to docs via mkdocs.
xls: Project-named subdirectory within the repository, in common Bazel-project style.
- build: Build
  macros that create XLS artifacts; e.g. convert DSL to IR, create test
  targets for DSL code, etc.
- codegen:
  Verilog AST (VAST) support to generate Verilog/SystemVerilog operations
  and FSMs. VAST is built up by components we call generators (e.g.
  PipelineGenerator, SequentialGenerator for FSMs) in the translation from
  XLS IR.
- common: "base"
  functionality that layers on top of standard library usage. Generally we
  use Abseil versions of base constructs wherever
  possible.
- contrib/xlscc:
  Experimental C++ syntax support that targets XLS IR (alternative path to
  DSLX) developed by a sister team at Google, sharing the same open source
  / testing flow as the rest of the XLS project. May be of particular
  interest for teams with existing C++ HLS code bases.
- data_structures:
  Generic data structures used in XLS that augment standard libraries;
  e.g. BDDs, union find, min cut, etc.
- delay_model:
  Functionality to characterize, describe, and interpolate data delay for
  XLS IR operations on a target backend process. Already-characterized
  descriptions are placed in xls/estimators/delay_model/models and can
  be referred to via command line flags.
- dslx: A DSL
  (called "DSLX") that mimics Rust, while being an immutable
  expression-language dataflow DSL with hardware-oriented features; e.g.
  arbitrary bitwidths, entirely fixed size objects, fully analyzeable call
  graph. XLS team has found dataflow DSLs are a good fit to describe
  hardware as compared to languages designed assume von Neumann style
  computation.
- fuzzer: A
  whole-stack multiprocess fuzzer that generates programs at the DSL level
  and cross-compares different execution engines (DSL interpreter, IR
  interpreter, IR JIT, code-generated-Verilog simulator). Designed so that
  it can easily be run on different nodes in a cluster simultaneously and
  accumulate shared findings.
- examples:
  Example computations that are tested and executable through the XLS
  stack.
- experimental:
  Artifacts captured from experimental explorations.
- interpreter:
  Interpreter for XLS IR - useful for debugging and exploration. For cases
  needing throughput, consider using the JIT (below).
- ir: XLS IR
  definition, text parser/formatter, and facilities for abstract
  evaluation.
- jit: LLVM-based JIT
  for XLS IR. Enables native-speed execution of DSLX and XLS IR programs.
- modules:
  Hardware building block DSLX "libraries" (outside the DSLX standard
  library) that may be easily reused or instantiated in a broader design.
- netlist:
  Libraries that parse/analyze/interpret netlist-level descriptions, as
  are generally given in simple structural Verilog with an associated cell
  library.
- passes: Passes
  that run on the XLS IR as part of optimization, before scheduling / code
  generation.
- scheduling:
  Scheduling algorithms, determine when operations execute (e.g. which
  pipeline stage) in a clocked design.
- simulation:
  Code that wraps Verilog simulators and generates Verilog testbenches for
  XLS computations. iverilog is
  currently used to simulate as it supports non-synthesizable testbench
  constructs.
- solvers:
  Converters from XLS IR into SMT solver input, such that formal proofs
  can be run on XLS computations; e.g. Logical Equalence Checks between
  XLS IR and a netlist description. Z3
  is used as the solver engine.
- synthesis:
  Interface that wraps backend synthesis flows, such that tools can be
  retargeted e.g. between ASIC and FPGA flows.
- tests:
  Integration tests that span various top-level components of the XLS
  project.
- tools:
  Many tools that work with the XLS
  system and its libraries in a decomposed way via command line
  interfaces.
- uncore_rtl:
  Helper RTL that interfaces XLS-generated blocks with device top-level
  for e.g. FPGA experiments.
- visualization:
  Visualization tools to inspect the XLS compiler/system interactively.
  See IR visualization.

Community

Discussions about XLS - development, debugging, usage, etc:

Ideally happen in the XLS repo GitHub
discussions
But, if you feel email is a better venue for the discussion, there is also an
xls-dev mailing list -- please prefer
GitHub discussions if possible as they are searchable and can be
easily cross-referenced and converted to the issue tracker

Contributors

The following are contributors to the XLS project, see our contributing documentation and good first issues if you're interested in contributing, or reach out via GitHub discussions!

Package Rankings

Top 8.65% on Proxy.golang.org

Badges

Extracted from project README

Related Projects

serenity

The Serenity Operating System 🐞

02 Dec 2018 28,800