Shurui Li

Shurui Li

Ph.D. Candidate at UCLA, Specialized in Deep Learning Accelerator, Optical Neural Network, and Computer Architecture

Publications

ReFOCUS: Reusing Light for Efficient Fourier Optics-Based Photonic Neural Network Accelerator

Published in IEEE/ACM International Symposium on Microarchitecture (MICRO), 2023

Follow up work of PhotoFourier, introduced optical buffer and WDM to further improve power efficiency of JTC-based neural network accelerator.

Recommended citation: Li, S., Yang, H., Wong, C.W., Sorger, V.J. and Gupta, P., 2023, October. ReFOCUS: Reusing Light for Efficient Fourier Optics-Based Photonic Neural Network Accelerator. In Proceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture (pp. 569-583). https://dl.acm.org/doi/proceedings/10.1145/3613424

Training Neural Networks for Execution on Approximate Hardware

Published in TinyML Research Symposium (TinyML), 2023

Proposed training optimizations to significantly improve the training time for neural networks to be executed on approximate hardware, including analog computing, stochastic computing, and approximate arithmetic.

Recommended citation: Li, T., Li, S. and Gupta, P., 2023. Training Neural Networks for Execution on Approximate Hardware. arXiv preprint arXiv:2304.04125. https://arxiv.org/pdf/2304.04125.pdf

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

Published in IEEE International Symposium on High-Performance Computer Architecture (HPCA), 2023

An architecture paper for energy-efficient and high-performance JTC-based neural network accelerator.

Recommended citation: Li, S., Yang, H., Wong, C.W., Sorger, V.J. and Gupta, P., 2023, February. Photofourier: A photonic joint transform correlator-based neural network accelerator. In 2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA) (pp. 15-28). IEEE. https://ieeexplore.ieee.org/document/10070931

High‐Throughput Multichannel Parallelized Diffraction Convolutional Neural Network Accelerator

Published in Laser & Photonics Reviews, 2022

An improved version of programmable DMD-based free-space optical neural network accelerator, offers more than 10X throughput improvement.

Recommended citation: Hu, Z., Li, S., Schwartz, R.L., Solyanik‐Gorgone, M., Miscuglio, M., Gupta, P. and Sorger, V.J., 2022. High‐Throughput Multichannel Parallelized Diffraction Convolutional Neural Network Accelerator. Laser & Photonics Reviews, p.2200213. https://onlinelibrary.wiley.com/doi/full/10.1002/lpor.202200213

4F optical neural network acceleration: an architecture perspective

Published in SPIE AI and Optical Data Sciences III, 2022

An invited paper about architecture and optimization of free-space 4F-based optical neural networks, which includes the channel tiling method to improve utilization and the performance comparison and projection.

Recommended citation: Li, S. and Gupta P., 2022, March. 4F optical neural network acceleration: an architecture perspective. In AI and Optical Data Sciences III (Vol. 12019, pp. 77-84). SPIE. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12019/120190B/4F-optical-neural-network-acceleration-an-architecture-perspective/10.1117/12.2614731.full?SSO=1

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

Published in Conference on Machine Learning and Systems (MLSys), 2022

This paper present a framework to compress neural networks and execute them with arbitrary precision on resource constrained processors through weight sharing, bit-serial computation, and look up tables.

Recommended citation: Li, S. and Gupta, P., 2022. Bit-serial Weight Pools: Compression and Arbitrary Precision Execution of Neural Networks on Resource Constrained Processors. Proceedings of Machine Learning and Systems, 4, pp.238-250. https://proceedings.mlsys.org/paper_files/paper/2022/file/502e4a16930e414107ee22b6198c578f-Paper.pdf

Channel Tiling for Improved Performance and Accuracy of Optical Neural Network Accelerators

Published in ArXiV, 2021

The original channel tiling paper, which proposed a tiling method to significanlty improve the utilization of free-space optical neural network accelerators.

Recommended citation: Li, S., Miscuglio, M., Sorger, V.J. and Gupta, P., 2020. Channel Tiling for Improved Performance and Accuracy of Optical Neural Network Accelerators. arXiv preprint arXiv:2011.07391. https://arxiv.org/pdf/2011.07391.pdf