Abstract:
Designing computing-in-memory (CIM) chips with synaptic plasticity can potentially support energy-efficient on-chip learning in edge devices for rapid local task adaptation. Its silicon implementation is challenging as it requires hybridizing nonvolatile and volatile memory (VM) and customized computational operations. In this work, we propose a plastic CIM (P-CIM) macro featuring: 1) …