Search Results for author: Shurui Li

Found 7 papers, 0 papers with code

SWIS -- Shared Weight bIt Sparsity for Efficient Neural Network Acceleration

no code implementations1 Mar 2021 Shurui Li, Wojciech Romaszkan, Alexander Graening, Puneet Gupta

Quantization is spearheading the increase in performance and efficiency of neural network computing systems making headway into commodity hardware.

Efficient Neural Network Quantization +1

Revisiting the dynamics of Bose-Einstein condensates in a double well by deep learning with a hybrid network

no code implementations25 Apr 2021 Shurui Li, Jianqin Xu, Jing Qian, Weiping Zhang

Deep learning, accounting for the use of an elaborate neural network, has recently been developed as an efficient and powerful tool to solve diverse problems in physics and other sciences.

High Throughput Multi-Channel Parallelized Diffraction Convolutional Neural Network Accelerator

no code implementations23 Dec 2021 Zibo Hu, Shurui Li, Russell L. T. Schwartz, Maria Solyanik-Gorgone, Mario Miscuglio, Puneet Gupta, Volker J. Sorger

Convolutional neural networks are paramount in image and signal processing including the relevant classification and training tasks alike and constitute for the majority of machine learning compute demand today.

Decision Making Vocal Bursts Intensity Prediction

Bit-serial Weight Pools: Compression and Arbitrary Precision Execution of Neural Networks on Resource Constrained Processors

no code implementations25 Jan 2022 Shurui Li, Puneet Gupta

Applications of neural networks on edge systems have proliferated in recent years but the ever-increasing model size makes neural networks not able to deploy on resource-constrained microcontrollers efficiently.

PhotoFourier: A Photonic Joint Transform Correlator-Based Neural Network Accelerator

no code implementations10 Nov 2022 Shurui Li, Hangbo Yang, Chee Wei Wong, Volker J. Sorger, Puneet Gupta

The last few years have seen a lot of work to address the challenge of low-latency and high-throughput convolutional neural network inference.

Training Neural Networks for Execution on Approximate Hardware

no code implementations8 Apr 2023 Tianmu Li, Shurui Li, Puneet Gupta

Approximate computing methods have shown great potential for deep learning.

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