README.md

Implementation in Chisel of Ternary Weight Networks

This paper creates an FPGA implementation of https://arxiv.org/abs/1605.04711 It implements a network with half the number of convolutional filters as in this paper achieving 91.9% on CIFAR10

Also checkout our more recent work here

Network architecture

conv1 => 64x3x3x3

conv2 => 64x3x3x64

mp1 => 2x2 kernel with stride 2

conv3 => 128x3x3x64

conv4 => 128x3x3x128

mp2 => 2x2 kernel with stride 2

conv5 => 256x3x3x128

conv6 => 256x3x3x256

mp3 => 2x2 kernel with stride 2

dense => 4096x1024

dense => 1024x10

Running

The complete verilog, including constraints is included in all_code.tgz An IP block is also available in sydney.edu.au_AWS_VGG_AWSVggWrapper_with_dense_1.0.zip and sydney.edu.au_AWS_VGG_AWSVggWrapper_no_dense_1.0.zip The complete network with dense layers did not fit on the FPGA, so the IP core without them is included as well To generate, run the script generate.sh, note however it takes a long time (8hrs+) and alot of memory (250GB)

FPGA version

There is no I/O included in this script but it targeted the AWS-F1 instance FPGA with part number xcvu9p-flgb2104-2-i

Citation

Please cite:

@article{tridgell2019unrolling,
  title={Unrolling Ternary Neural Networks},
  author={Tridgell, Stephen and Kumm, Martin and Hardieck, Martin and Boland, David and Moss, Duncan and Zipf, Peter and Leong, Philip HW},
  journal={ACM Transactions on Reconfigurable Technology and Systems (TRETS)},
  volume={12},
  number={4},
  pages={22},
  year={2019},
  publisher={ACM}
}