SpikeSim: An End-to-End Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networks-Reference-Cited by-同舟云学术

SpikeSim: An End-to-End Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networks

Published:2023-11 Issue:11 Volume:42 Page:3815-3828
ISSN:0278-0070
Container-title:IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
language:
Short-container-title:IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

Author:

Moitra Abhishek¹^ORCID,Bhattacharjee Abhiroop¹^ORCID,Kuang Runcong²^ORCID,Krishnan Gokul³^ORCID,Cao Yu²^ORCID,Panda Priyadarshini¹^ORCID

Affiliation:

1. Department of Electrical Engineering, Yale University, New Haven, CT, USA

2. School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ, USA

3. Meta Reality Labs, Redmond, WA, USA

Funder

JUMP2.0 Center and CBRIC a JUMP1.0 Center

DARPA and SRC

Google Research Scholar Award

National Science Foundation CAREER Award, TII

DARPA AI Exploration (AIE) Program

DoE MMICC Center SEACROGS

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Software

Link

Reference46 articles.

4. A free lunch from ANN: Towards efficient, accurate spiking neural networks calibration;li;arXiv 2106 06984,2021

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