no code implementations • 22 Jul 2022 • Yao Chen, Junhao Pan, Xinheng Liu, JinJun Xiong, Deming Chen
In this study, we propose HiKonv, a unified solution that maximizes the throughput of convolution on a given underlying processing unit with low-bitwidth quantized data inputs through novel bit-wise management and parallel computation.
no code implementations • 28 Dec 2021 • Xinheng Liu, Yao Chen, Prakhar Ganesh, Junhao Pan, JinJun Xiong, Deming Chen
Quantization for Convolutional Neural Network (CNN) has shown significant progress with the intention of reducing the cost of computation and storage with low-bitwidth data inputs.
2 code implementations • 22 Dec 2020 • Yichi Zhang, Junhao Pan, Xinheng Liu, Hongzheng Chen, Deming Chen, Zhiru Zhang
We design an efficient FPGA-based accelerator for our novel BNN model that supports the fractional activations.