A 65 nm General-Purpose Compute-in-Memory Processor Supporting Both General Programming and Deep Learning Tasks

Abstract

This work presents a special unified compute-in-memory (CIM) processor supporting both general-purpose computing and deep neural network (DNN) operations, referred to as the general-purpose CIM (GPCIM) processor. By implementing a unique CIM macro with two different bitcell arrays and a central compute unit (CCU), GPCIM can be reconfigured to a CIM DNN accelerator or a CIM vector central processing unit (CPU). By using special reconfigurability, dataflow, and support of a customized vector instruction set, GPCIM achieves SOTA performance for end-to-end deep learning tasks with enhanced CPU efficiency and data locality. A 65 nm test chip was fabricated demonstrating a 28.3 TOPS/W DNN macro efficiency and a best-in-class peak CPU efficiency of 802 GOPS/W. Benefit from a data locality flow, 37%–55% end-to-end latency reduction on artificial intelligence (AI)-related applications is achieved by eliminating inter-core data transfer in traditional heterogeneous system-on-chip (SoC). An averaged $17.8{\times }$ CPU energy efficiency improvement is achieved compared with vector RISC-V CPUs in the existing machine learning (ML) SoCs.