Hessian Aware Quantization of Spiking Neural Networks-Reference-Cited by-同舟云学术

Hessian Aware Quantization of Spiking Neural Networks

Published:2021-07-27 Issue: Volume: Page:
ISSN:
Container-title:International Conference on Neuromorphic Systems 2021
language:
Short-container-title:

Author:

Lui Hin Wai¹,Neftci Emre¹

Affiliation:

1. University of California, Irvine, United States

Funder

Korean Institute for Science and Technology

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3477145.3477158

Reference17 articles.

1. Loihi: A Neuromorphic Manycore Processor with On-Chip Learning;Davies M.;IEEE Micro PP,2018

2. Zhen Dong Zhewei Yao Yaohui Cai Daiyaan Arfeen Amir Gholami Michael W. Mahoney and Kurt Keutzer. 2019. HAWQ-V2: Hessian aware trace-weighted quantization of neural networks. Zhen Dong Zhewei Yao Yaohui Cai Daiyaan Arfeen Amir Gholami Michael W. Mahoney and Kurt Keutzer. 2019. HAWQ-V2: Hessian aware trace-weighted quantization of neural networks.

3. Steven K. Esser Jeffrey L. McKinstry Deepika Bablani Rathinakumar Appuswamy and Dharmendra S. Modha. 2019. Learned step size quantization. Steven K. Esser Jeffrey L. McKinstry Deepika Bablani Rathinakumar Appuswamy and Dharmendra S. Modha. 2019. Learned step size quantization.

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Exploring 8-Bit Arithmetic for Training Spiking Neural Networks;2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS);2024-07-29

2. SQUAT: Stateful Quantization-Aware Training in Recurrent Spiking Neural Networks;2024 Neuro Inspired Computational Elements Conference (NICE);2024-04-23

3. MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural Networks;2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC);2024-01-22

4. HyperSpikeASIC: Accelerating Event-Based Workloads With HyperDimensional Computing and Spiking Neural Networks;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2023-11

5. Sharing leaky-integrate-and-fire neurons for memory-efficient spiking neural networks;Frontiers in Neuroscience;2023-07-31