77 citations · 126 across the 7 of their papers we have counts for
8 papers
Logic Shrinkage: Learned FPGA Netlist Sparsity for Efficient Neural Network Inference
Erwei Wang, James J. Davis, Georgios-Ilias Stavrou +3
FPGA-specific DNN architectures using the native LUTs as independently trainable inference operators have been shown to achieve favorable area-accuracy and energy-accuracy tradeoff…
Accelerating Recurrent Neural Networks for Gravitational Wave Experiments
Zhiqiang Que, Erwei Wang, Umar Marikar +10
This paper presents novel reconfigurable architectures for reducing the latency of recurrent neural networks (RNNs) that are used for detecting gravitational waves. Gravitational i…
Enabling Binary Neural Network Training on the Edge
Erwei Wang, James J. Davis, Daniele Moro +6
The ever-growing computational demands of increasingly complex machine learning models frequently necessitate the use of powerful cloud-based infrastructure for their training. Bin…
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…
Accuracy to Throughput Trade-offs for Reduced Precision Neural Networks on Reconfigurable Logic
Jiang Su, Nicholas J. Fraser, Giulio Gambardella +5
Modern CNN are typically based on floating point linear algebra based implementations. Recently, reduced precision NN have been gaining popularity as they require significantly les…