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

• Main Authors: Zhu, Shien, Huai, Shuo, Xiong, Guochu, Liu, Weichen
• 其他作者: School of Computer Science and Engineering
• 格式: Conference or Workshop Item
• 語言: English
• 出版: 2023
• 主題: 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
• 在線閱讀: https://hdl.handle.net/10356/170218

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...