Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/tensil-ai/tensil

Open source machine learning accelerators
https://github.com/tensil-ai/tensil

artificial-intelligence asic fpga hdl machine-learning scala silicon

Last synced: 3 months ago
JSON representation

Open source machine learning accelerators

Awesome Lists containing this project

README 

        
Tensil

==========================



[![Build status](https://badge.buildkite.com/11c53cfb0931de5a89dfece9634fe3a5f5cefc9525e1162e1a.svg?branch=main)](https://buildkite.com/tensil/tensil)



## Tensil toolchain flow



![Flow](https://s3.us-west-1.amazonaws.com/downloads.tensil.ai/doc/flow.png)



## Tutorials



For in-depth end-to-end instructions check our tutorials.



- [Learn how to combine Tensil and TF-Lite to run YOLO on Ultra96](https://www.tensil.ai/docs/tutorials/yolo-ultra96v2/)

- [Learn Tensil with ResNet and PYNQ Z1](https://www.tensil.ai/docs/tutorials/resnet20-pynqz1/)

- [Learn Tensil with ResNet and Ultra96](https://www.tensil.ai/docs/tutorials/resnet20-ultra96v2/)

- [Learn how to build speech controlled robot with Tensil and Arty A7 - Part I](https://www.tensil.ai/docs/tutorials/speech-robot-part1/)

- [Learn how to build speech controlled robot with Tensil and Arty A7 - Part II](https://www.tensil.ai/docs/tutorials/speech-robot-part2/)

- [Getting Tensil to run ResNet at 300 FPS on ZCU104](https://www.tensil.ai/docs/tutorials/resnet20-zcu104/)



## Documentation



For reference documentation see our [website](https://www.tensil.ai/docs/).



## Setup



1. Pull and run [Tensil docker container](https://hub.docker.com/r/tensilai/tensil) (see below);

2. Download and install [Xilinx Vitis or Vivado](https://www.xilinx.com/support/download.html);

3. Download and install [Xilinx PYNQ](http://www.pynq.io/board.html) for your FPGA development platform;

4. Copy Tensil PYNQ driver `drivers/tcu_pynq` to `/home/xilinx/tcu_pynq` on your FPGA development platform.



## Pull and run docker container



```

docker pull tensilai/tensil

docker run -u $(id -u ${USER}):$(id -g ${USER}) -v $(pwd):/work -w /work -it tensilai/tensil bash

```



## Compile AI/ML model



Compile AI/ML model (ResNet20 v2 CIFAR) for specific TCU architecture and FPGA development platform, [PYNQ Z1](https://digilent.com/shop/pynq-z1-python-productivity-for-zynq-7000-arm-fpga-soc/) in this example.



#### From ONNX



```

tensil compile -a /demo/arch/pynqz1.tarch -m /demo/models/resnet20v2_cifar.onnx -o "Identity:0" -s true

```



#### From frozen TensorFlow graph



```

tensil compile -a /demo/arch/pynqz1.tarch -m /demo/models/resnet20v2_cifar.pb -o "Identity" -s true

```



#### Other ML frameworks are supported by converting to ONNX



- [TensorFlow and Tflite](https://github.com/onnx/tensorflow-onnx/blob/master/README.md)

- [Pytorch](https://pytorch.org/docs/stable/onnx.html)

- [Other](https://onnx.ai/supported-tools.html)



## Run bit accurate Tensil emulator



```

tensil emulate -m resnet20v2_cifar_onnx_pynqz1.tmodel -i /demo/models/data/resnet_input_1x32x32x8.csv

```



## Make Verilog RTL



Make Verilog RTL for specific TCU architecture and FPGA development platform, [PYNQ Z1](https://digilent.com/shop/pynq-z1-python-productivity-for-zynq-7000-arm-fpga-soc/) in this example.



```

tensil rtl -a /demo/arch/pynqz1.tarch -s true

```



## Create Vivado design



Create Vivado design for specific FPGA development platform. We include detailed steps in our [PYNQ Z1 tutorial](https://www.tensil.ai/docs/tutorials/resnet20-pynqz1/). If you get stuck, we can help! Please reach out to us at [[email protected]](mailto:[email protected]) or in [Discord](https://discord.gg/TSw34H3PXr).



![PYNQ Z1 design](https://s3.us-west-1.amazonaws.com/downloads.tensil.ai/doc/pynqz1_design.png)



## Run AI/ML model on FPGA



Use PYNQ and Jupyter notebooks to run AI/ML model on FPGA. (See in `notebooks`.)



![Resnet on PYNQ](https://s3.us-west-1.amazonaws.com/downloads.tensil.ai/doc/resnet20_on_pynq.png)



## For maintainers



### Additional setup steps



1. Download and install [OpenJDK 11 from Azul](https://www.azul.com/downloads/?version=java-11-lts&package=jdk);

2. Download and install [Verilator](https://verilator.org/guide/latest/install.html);

3. Download test models:



```

wget https://github.com/tensil-ai/tensil-models/archive/main.tar.gz

tar xf main.tar.gz

mv tensil-models-main models

rm main.tar.gz

```



### Run RTL tool from source code



```

./mill rtl.run -a ./arch/pynqz1.tarch -s true

```



### Run compiler from source code



```

./mill compiler.run -a ./arch/pynqz1.tarch -m ./models/resnet20v2_cifar.onnx -o "Identity:0" -s true

```



### Run emulator from source code



```

./mill emulator.run -m resnet20v2_cifar_onnx_pynqz1.tmodel -i ./models/data/resnet_input_1x32x32x8.csv

```



### Run full test suite



```

./mill __.test -l org.scalatest.tags.Slow

```



To run a single RTL test in, for example, the Accumulator module, and also output a VCD file, do:



```

./mill rtl.test.testOnly tensil.tcu.AccumulatorSpec -- -DwriteVcd=true -z "should accumulate values"

```



### View VCD files



To view the latest VCD file generated:



```

./scripts/gtkwave/display-latest-vcd.py

```



To view a specific VCD file:



```

./scripts/gtkwave/display-vcd.sh 

```



### Build and push AWS ECS image for web compiler (Tensil Explorer)



```

docker build -f docker/web/Dockerfile -t tensil-web-compiler .

aws ecr get-login-password | docker login --username AWS --password-stdin .dkr.ecr..amazonaws.com

docker tag tensil-web-compiler .dkr.ecr..amazonaws.com/tf2rtl-web-compiler

docker push .dkr.ecr..amazonaws.com/tf2rtl-web-compiler

```



## Get help



- Say hello and ask a question on our [Discord](https://discord.gg/TSw34H3PXr)

- Email us at [[email protected]](mailto:[email protected])