77 citations · 126 across the 12 of their papers we have counts for
4 papers · 1 filter
Automatic Generation of Multi-precision Multi-arithmetic CNN Accelerators for FPGAs
Yiren Zhao, Xitong Gao, Xuan Guo +6
Modern deep Convolutional Neural Networks (CNNs) are computationally demanding, yet real applications often require high throughput and low latency. To help tackle these problems,…
LUTNet: Learning FPGA Configurations for Highly Efficient Neural Network Inference
Erwei Wang, James J. Davis, Peter Y. K. Cheung +1
Research has shown that deep neural networks contain significant redundancy, and thus that high classification accuracy can be achieved even when weights and activations are quanti…
LUTNet: Rethinking Inference in FPGA Soft Logic
Erwei Wang, James J. Davis, Peter Y. K. Cheung +1
Research has shown that deep neural networks contain significant redundancy, and that high classification accuracies can be achieved even when weights and activations are quantised…
Deep Neural Network Approximation for Custom Hardware: Where We've Been, Where We're Going
Erwei Wang, James J. Davis, Ruizhe Zhao +5
Deep neural networks have proven to be particularly effective in visual and audio recognition tasks. Existing models tend to be computationally expensive and memory intensive, howe…