Open-source GUINNESS makes FPGA-accelerated, binarized neural networks easy to pour right from the SDSoC tap
[ ]A new open-source tool named GUINNESS makes it easy for you to develop binarized (2-valued) neural networks (BNNs) for Zynq SoCs and Zynq UltraScale+ MPSoCs using the SDSoC Development Environment. GUINNESS is a GUI-based tool that uses the Chainer deep-learning framework to train a binarized CNN. In a paper titled On-Chip Memory Based Binarized Convolutional Deep Neural Network Applying Batch Normalization Free Technique on an FPGA presented at the recent 2017 IEEE International Parallel and Distributed Processing Symposium Workshops, authors Haruyoshi Yonekawa and Hiroki Nakahara describe a system they developed to implement a binarized CNN for the VGG-16 benchmark on the Xilinx ZCU102 Eval Kit, which is based on a Zynq UltraScale+ ZU9EG MPSoC. Nakahara presented the GUINNESS tool again this week at FPL2017 in Ghent, Belgium.</p>
According to the IEEE paper, the Zynq-based BNN is 136.8x faster and 44.7x more power efficient than the same CNN running on an ARM Cortex-A57 processor. Compared to the same CNN running on an Nvidia Maxwell GPU, the Zynq-based BNN is 4.9x faster and 3.8x more power efficient.
GUINNESS is now available on GitHub
