Ruokai Yin

PhD candidate, Electrical Engineering, Yale University

E-mail: ruokai.yin@yale.edu

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Bio

Ruokai is currently a 3rd year Ph.D. student in the Department of Electrical Engineering at Yale University, where he is advised by Prof. Priyadarshini Panda.

His research focuses on various computing schemes, with a particular emphasis on neuromorphic computing, as enablers for energy-efficient machine learning. He is especially interested in designing low-power computer architectures and systems tailored to these computing schemes. Additionally, he works on co-designing hardware-aware compression algorithms for neural networks, specifically aimed at enhancing energy efficiency during deployment.

Prior to joining Yale, he earned his B.S. from the University of Wisconsin-Madison, majored in in Electrical Engineering, Computer Science, and Mathematics. During his undergraduate, he worked with Prof. Joshua San Miguel on designing computer architectures for stochastic computing.

Washington, D.C., Apr 2022

News

  • 07, 2024: » LoAS is accepted to MICRO 2024.
  • 06, 2024: » Starting the position of Research Intern in ASIC team at Cerebras System Inc.
  • 03, 2024: » u-Ticket is accepted to IEEE TETCI.
  • 10, 2023: » MINT is nominated for best paper award at ASP-DAC 2024.
  • 09, 2023: » MINT is accepted to ASP-DAC 2024.

Selected Publications

LoAS: Fully Temporal-Parallel Dataflow for Dual-Sparse Spiking Neural Networks
Ruokai Yin, Youngeun Kim, Di Wu, Priyadarshini Panda
57th IEEE/ACM International Symposium on Microarchitecture (MICRO), Nov, 2024
[paper]   [code]


TT-SNN: Tensor Train Decomposition for Efficient Spiking Neural Network Training
Donghyun Lee, Ruokai Yin, Youngeun Kim, Abhishek Moitra, Yuhang Li, Priyadarshini Panda
27th Design, Automation and Test in Europe Conference (DATE), Mar, 2024
[paper]   [code]


Workload-Balanced Pruning for Sparse Spiking Neural Networks
Ruokai Yin, Youngeun Kim, Yuhang Li, Abhishek Moitra, Nitin Satpute, Anna Hambitzer, Priyadarshini Panda
IEEE Transactions on Emerging Topics in Computational Intelligence, 2024
[paper]   [code]


Are SNNs Truly Energy-efficient? - A Hardware Perspective
Abhiroop Bhattacharjee*, Ruokai Yin*, Abhishek Moitra*, Priyadarshini Panda (* Equal Contribution)
2024 IEEE International Conference on Acounstics, Speech, and Signal Processing (ICASSP), April, 2024
[paper]   [code]


MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural Networks
Ruokai Yin, Yuhang Li, Abhishek Moitra, Priyadarshini Panda
29th Asia and South Pacific Design Automation Conference (ASP-DAC) (Nominated as Best Paper), Jan, 2024
[paper]   [code]   [slides]


Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networks
Youngeun, Yuhang Li, Abhishek Moitra, Ruokai Yin, Priyadarshini Panda
Frontiers in Neuroscience, 2023
[paper]   [code]


Efficient Human Activity Recognition with Spatio-Temporal Spiking Neural Networks
Yuhang Li, Ruokai Yin, Youngeun Kim, Priyadarshini Panda
Frontiers in Neuroscience, 2023
[paper]   [code]


Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural Networks
Yuhang Li, Ruokai Yin, Hyoungseob Park, Youngeun Kim, Priyadarshini Panda
36th NeurIPS Workshop on Learning from Time Series for Health (Selected as Spotlight Paper), Dec, 2022
[paper]   [code]


SATA: Sparsity-Aware Training Accelerator for Spiking Neural Networks
Ruokai Yin, Abhishek Moitra, Abhiroop Bhattacharjee, Youngeun Kim, Priyadarshini Panda
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2022
[paper]   [code]


Exploring Lottery Ticket Hypothesis in Spiking Neural Networks
Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, Ruokai Yin, Priyadarshini Panda
18th European Conference on Computer Vision (ECCV), Oct, 2022
[paper]   [code]


uGEMM: Unary Computing for GEMM Applications
Di Wu, Jingjie Li, Ruokai Yin, Hsuan Hsiao, Younghyun Kim, and Joshua San Miguel
IEEE Micro Top Picks, 2021
[paper]   [code]


Normalized Stability: A Cross-Level Design Metric for Early Termination in Stochastic Computing
Di Wu, Ruokai Yin, and Joshua San Miguel
Proceedings of the 26th Asia and South Pacific Design Automation Conference (ASPDAC), Jan, 2021
[paper]   [code]


In-Stream Correlation-Based Division and Bit-Inserting Square Root in Stochastic Computing
Di Wu, Ruokai Yin, and Joshua San Miguel
IEEE Design & Test, 2021
[paper]   [code]


uGEMM: Unary Computing Architecture for GEMM Applications
Di Wu, Jingjie Li, Ruokai Yin, Hsuan Hsiao, Younghyun Kim, and Joshua San Miguel
47th Annual International Symposium on Computer Architecture (ISCA), Jun, 2020
[paper]   [code]


Experience

July 2021-Present

Graduate Research Assistant, Intelligent Computing Lab
Yale University

Pursuing PhD degree with Prof. Priya Panda. Working on projects that improving the energy efficiency of neural networks, in particular, spiking neural networks.

June 2019-May 2021

Undergraduate Research Assistant, STACS Lab
University of Wisconsin-Madison

Advised by Prof. Joshua San Miguel. Worked on projects that applying unary computing to the deep neural networks. Construted a PyTorch-basede library for unary computing.

Teaching

2023 Fall

Teaching Fellow, EENG 439 Neural Networks and Learning Systems
Yale University

Instructor: Prof. Priya Panda. Course Description.

2023 Spring

Teaching Fellow, EENG 348/CPSC 338: Digital Systems
Yale University

Instructor: Prof. Rajit Manohar. Course Description.