<b>HyperSpikeASIC</b>: Accelerating Event-Based Workloads With HyperDimensional Computing and Spiking Neural Networks-Reference-Cited by-同舟云学术

HyperSpikeASIC: Accelerating Event-Based Workloads With HyperDimensional Computing and Spiking Neural Networks

Published:2023-11 Issue:11 Volume:42 Page:3997-4010
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:

Zhang Tianqi¹^ORCID,Morris Justing²,Stewart Kenneth³^ORCID,Lui Hin Wai³,Khaleghi Behnam¹,Thomas Anthony¹,Goncalves-Marback Thiago¹,Aksanli Baris⁴^ORCID,Neftci Emre O.³,Rosing Tajana¹

Affiliation:

1. Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA, USA

2. Department of Computer Science and Information Systems, California State University San Marcos, San Marcos, CA, USA

3. Department of Computer Science, University of California at Irvine, Irvine, CA, USA

4. Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA, USA

Funder

CRISP, one of six centers in JUMP, an SRC Program sponsored by DARPA, PRISM and CoCoSys, centers in JUMP 2.0;

SRC-Global Research Collaboration

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Link

https://ieeexplore.ieee.org/ielam/43/10288105/10090457-aam.pdf

Reference71 articles.

1. Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors

2. Motor control by precisely timed spike patterns

3. Unraveling the paradox of intensity-dependent DVS pixel noise;graca;arXiv 2109 08640,2021

4. Hessian Aware Quantization of Spiking Neural Networks

5. HDnn-PIM: Efficient in Memory Design of Hyperdimensional Computing with Feature Extraction

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