An Energy-Efficient Computing-in-Memory Neuromorphic System with On-Chip Training

• Published in: 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) pp. 1 - 4
• Main Authors: Zhao, Zhao, Wang, Yuan, Zhang, Xinyue, Cui, Xiaoxin, Huang, Ru
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2019
• Subjects: analog computation; bio-plasticity synapses; computing-in-memory (CIM); multiply-and-accumulate (MAC); neuromorphic computing system; Neuromorphics; Neurons; on-chip training; Random access memory; Static VAr compensators; Synapses; Testing; Training

The aim of neuromorphic computing system is to implement the computational power and efficiency of the human brain. Computing-in-memory (CIM) is a promising and energy-efficient way to perform intensive computations, whose structure is similar to human brain synapse. A 8.78TOPS/W biologically-inspir...