Recovering accuracy of RRAM-based CIM for binarized neural network via Chip-in-the-loop training

• Main Authors: Chong, Yi Sheng, Goh, Wang Ling, Ong, Yew Soon, Nambiar, Vishnu P., Do, Anh Tuan
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2022
• Subjects: Engineering::Electrical and electronic engineering; Binarized Neural Network; Resistive Random Access Memory; Compute-in-Memory
• Online Access: https://hdl.handle.net/10356/159308
• Institution: Nanyang Technological University

Resistive random access memory (RRAM) based computing-in-memory (CIM) is attractive for edge artificial intelligence (AI) applications, thanks to its excellent energy efficiency, compactness and high parallelism in matrix vector multiplication (MatVec) operations. However, existing RRAM-based CIM de...