iMAT: energy-efficient in-memory acceleration for ternary neural networks with sparse dot product

iMAT: energy-efficient in-memory acceleration for ternary neural networks with sparse dot product

Ternary Neural Networks (TNNs) achieve an excellent trade-off between model size, speed, and accuracy, quantizing weights and activations into ternary values {+1, 0, -1}. The ternary multiplication operations in TNNs equal light-weight bitwise operations, favorably in In-Memory Computing (IMC) platf...

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Bibliographic Details
Main Authors: Zhu, Shien, Huai, Shuo, Xiong, Guochu, Liu, Weichen
Other Authors: School of Computer Science and Engineering
Format: Conference Paper
Language:English
Published: 2023
Subjects:
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Computer science and engineering::Computer systems organization::Special-purpose and application-based systems
Engineering::Electrical and electronic engineering::Computer hardware, software and systems
In-Memory Computing
Ternary Neural Network
Software-Hardware Co-Design
Online Access:https://hdl.handle.net/10356/170218

Internet

https://hdl.handle.net/10356/170218

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