CFU Playground

Want a faster ML processor? Do it yourself!

This project provides a framework that an engineer, intern, or student can use to design and evaluate enhancements to an FPGA-based “soft” processor, specifically to increase the performance of machine learning (ML) tasks. The goal is to abstract away most infrastructure details so that the user can get up to speed quickly and focus solely on adding new processor instructions, exploiting them in the computation, and measuring the results.

This project enables rapid iteration on processor improvements -- multiple iterations per day.

This is how it works:

• Choose a TensorFlow Lite model; a quantized person detection model is provided, or bring your own.
• Execute the inference on the Arty FPGA board to get cycle counts per layer.
• Choose an TFLite operator to accelerate, and dig into that code.
• Design new instruction(s) that can replace multiple basic operations.
• Build a custom function unit (a small amount of hardware) that performs the new instruction(s).
• Modify the TFLite/Micro library kernel to use the new instruction(s), which are available as intrinsics with function call syntax.
• Rebuild the FPGA Soc, recompile the TFLM library, and rerun to measure improvement.

The focus here is performance, not demos. The inputs to the ML inference are canned/faked, and the only output is cycle counts. It would be possible to export the improvements made here to an actual demo, but currently no pathway is set up for doing so.

With the exception of Vivado, everything used by this project is open source.

Disclaimer: This is not an officially supported Google project. Support and/or new releases may be limited.

This is an early prototype of a ML exploration framework; expect a lack of documentation and occasional breakage. If you want to collaborate on building out this framework, reach out to [email protected]! See "Contribution guidelines" below.

Required hardware/OS

• One of the boards supported by LiteX Boards. Most of LiteX Boards targets should work.
  It has been tested on Arty A7-35T and Nexys Video Board.
• The only supported host OS is Linux (Debian / Ubuntu).

You don't need any board if you want to run Renode or Verilator simulation.

Assumed software

• Toolchain that depends on a chosen board.
  For Arty A7-35T it would be Vivado which must be manually installed. You always need a toolchain for a target that you choose.

If you want to try things out using Renode simulation, then you don't need either the board or toolchain. You can also perform Verilog-level cycle-accurate simulation with Verilator, but this is much slower.

Other required packages will be checked for and, if on a Debian-based system, automatically installed by the setup script below.

Setup

Clone this repo, cd into it, then get run:

scripts/setup

Use with board

The default board is Arty. If you want to use different board you must specify target, e.g. TARGET=digilent_nexys_video.

1. Build the SoC and load the bitstream onto Arty:

cd proj/proj_template
make prog

This builds the SoC with the default CFU from proj/proj_template. Later you'll copy this and modify it to make your own project.

2. Build a RISC-V program and execute it on the SoC that you just loaded onto the Arty:

make load

Use without board

If you don't have any board supported by LiteX Boards you can use Renode or Verilator to simulate it.

To use Renode to execute on a simulator on the host machine (no Vivado or Arty board required), execute:

make renode

To use Verilator to execute on a cycle-accurate RTL-level simulator (no Vivado or Arty board required), execute:

make PLATFORM=sim load

Most useful make flags

Option	Explanation	Example	Default
PLATFORM	Choose which SoC platform you want to build: hps or sim or common_soc	make bitstream PLATFORM=hps	common_soc
TARGET	Choose one of many targets from LiteX Boards repository, common_soc will take BaseSoC from specified target.py	make bitstream TARGET=nexys_video_board	digilent_arty
USE_VIVADO	Use Vivado toolchain	make bitstream USE_VIVADO=1	0
USE_SYMBIFLOW	Use Symbiflow toolchain	make bitstream USE_SYMBIFLOW=1	0
UART_SPEED	Choose UART baudrate	make bitstream UART_SPEED=115200	3686400
IGNORE_TIMING	Ignore timing contraints (only for Vivado)	make bitstream USE_VIVADO=1 IGNORE_TIMING=1	0

Underlying open-source technology

• LiteX: Open-source framework for assembling the SoC (CPU + peripherals)
• VexRiscv: Open-source RISC-V soft CPU optimized for FPGAs
• nMigen: Python toolbox for building digital hardware

Licensed under Apache-2.0 license

See the file LICENSE.

Contribution guidelines

If you want to contribute to CFU Playground, be sure to review the contribution guidelines. This project adheres to Google's code of conduct. By participating, you are expected to uphold this code.