5 citations · 8 across the 8 of their papers we have counts for
9 papers
CIMPool: Scalable Neural Network Acceleration for Compute-In-Memory using Weight Pools
Shurui Li, Puneet Gupta
Compute-in-memory (CIM) based neural network accelerators offer a promising solution to the Von Neumann bottleneck by computing directly within memory arrays. However, SRAM CIM fac…
Near-energy-free Photonic Fourier Transformation for Convolution Operation Acceler
Hangbo Yang, Nicola Peserico, Shurui Li +7
Convolutional operations are computationally intensive in artificial intelligence services, and their overhead in electronic hardware limits machine learning scaling. Here, we intr…
Training Neural Networks for Execution on Approximate Hardware
Tianmu Li, Shurui Li, Puneet Gupta
Approximate computing methods have shown great potential for deep learning. Due to the reduced hardware costs, these methods are especially suitable for inference tasks on battery-…
PhotoFourier: A Photonic Joint Transform Correlator-Based Neural Network Accelerator
Shurui Li, Hangbo Yang, Chee Wei Wong +2
The last few years have seen a lot of work to address the challenge of low-latency and high-throughput convolutional neural network inference. Integrated photonics has the potentia…
Bit-serial Weight Pools: Compression and Arbitrary Precision Execution of Neural Networks on Resource Constrained Processors
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-constraine…
High Throughput Multi-Channel Parallelized Diffraction Convolutional Neural Network Accelerator
Zibo Hu, Shurui Li, Russell L. T. Schwartz +4
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…