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 or Workshop Item
Language:English
Published: 2023
Subjects:
Online Access:https://hdl.handle.net/10356/170218
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Institution: Nanyang Technological University
Language: English