FAT: an in-memory accelerator with fast addition for ternary weight neural networks

Convolutional Neural Networks (CNNs) demonstrate excellent performance in various applications but have high computational complexity. Quantization is applied to reduce the latency and storage cost of CNNs. Among the quantization methods, Binary and Ternary Weight Networks (BWNs and TWNs) have a uni...

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Bibliographic Details
Main Authors: Zhu, Shien, Duong, Luan H. K., Chen, Hui, Liu, Di, Liu, Weichen
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2022
Subjects:
Online Access:https://hdl.handle.net/10356/162483
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Institution: Nanyang Technological University
Language: English
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