https://github.com/19801201/SpinalHDL_CNN_Accelerator

CNN accelerator implemented with Spinal HDL
https://github.com/19801201/SpinalHDL_CNN_Accelerator

cnn fpga object-detection spinalhdl xilinx yolo

Last synced: 3 months ago
JSON representation

CNN accelerator implemented with Spinal HDL

• Host: GitHub
• URL: https://github.com/19801201/SpinalHDL_CNN_Accelerator
• Owner: 19801201
• License: gpl-3.0
• Created: 2021-12-27T02:24:12.000Z (about 3 years ago)
• Default Branch: dev
• Last Pushed: 2024-01-29T12:46:19.000Z (about 1 year ago)
• Last Synced: 2024-08-02T01:20:46.832Z (7 months ago)
• Topics: cnn, fpga, object-detection, spinalhdl, xilinx, yolo
• Language: Scala
• Homepage:
• Size: 2.25 MB
• Stars: 126
• Watchers: 6
• Forks: 33
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-yolo-object-detection - 19801201/SpinalHDL_CNN_Accelerator
• awesome-yolo-object-detection - 19801201/SpinalHDL_CNN_Accelerator

README

        
# About SpinalHDL_CNN_Accelerator

![GitHub](https://img.shields.io/github/license/liuwei9/spinal_yolo)

[中文说明](./README_CN.md)

## Implementing accelerator supporting various neural networks in SpinalHDL

- This repository is implementing common operators in CNN in SpinalHDL 1.7.3.It can be guided by compiler to generate accelerators for various neural networks.

- This repository aims to implement a general accelerator at the SpinalHDL level and a special accelerator at the Verilog level.

- This repository implements an Npu module, which can be put into the project,as shown in the figure.

![block_design](./img/block_design.png)

- The resource occupation in FPGA is shown in the figure below:

![resource.png](./img/resource.png)

## Description

This repository hosts a CNN Accelerator implementation written in SpinalHDL. Here are some specs :

- Easy to use

- Complete implementation of convolution, quantization and shape 

- Configurable parametric interface

- Optional convolution kernel type

- The FPGA resource occupation is optimized at the code level

- Implementation of rich tool class code

- Access control of DMA and AXI

- Automatically generate TCL files for instantiating Xilinx IP

- Top-level files are easy to run

- Implementation of the simulation of convolution

## RTL code generation

You can find three runnable top modules in:

- `src/main/scala/top.scala`

- `src/main/scala/Npu.scala`

- `src/test/scala/TbConv.scala`

NOTES:

- It could take time when you run it.

- `top.scala` is used to implement convolution.

- `Npu.scala` is used to implement complete project process.

- `TbConv.scala` is used to implement the simulation of convolution.

- Before you run the `TbConv.scala`,you should prepare files with feature and weight in advance.