# spooNN

**Repository Path**: research-project-team/spooNN

## Basic Information

- **Project Name**: spooNN
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: AGPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-10-21
- **Last Updated**: 2021-10-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# spooNN

![picture](spooNN.png)

This is a repository for FPGA-based neural network inference, that delivered the highest FPS in the international contest for object detection as part of Design Automation Conference 2018 and 2019 (https://www.dac.com/content/2018-system-design-contest). The contents of spooNN enable an end-to-end capability to perform inference on FPGAs; starting from training scripts using Tensorflow to deployment on hardware. Target hardware platforms are PYNQ (http://www.pynq.io/) and ULTRA96 (https://www.96boards.org/product/ultra96/).

![picture](DAC_rankings.jpg)
2018: The final rankings are published at http://www.cse.cuhk.edu.hk/~byu/2018-DAC-SDC/index.html

![picture](DAC_rankings2019.jpg)
2019: The final rankings are published at http://www.cse.cuhk.edu.hk/~byu/2019-DAC-SDC/index.html

## Repo organization
- hls-nn-lib: A neural network inference library implemented in C for Vivado High Level Synthesis (HLS).
- mnist-cnn: helloworld project, showing an end-to-end flow (training, implementation, FPGA deployment) for MNIST handwritted digit classification with a convolutional neural network.
- halfsqueezenet (targets PYNQ): The object detection network, that ranked second in DAC 2018 contest, delivering the highest FPS at lowest power consumption for object detection.
- recthalfsqznet (targets ULTRA96): The object detection network, that ranked second in DAC 2019 contest, delivering the highest FPS at lowest power consumption for object detection.