A Crossbar-Based In-Memory Computing Architecture

Abstract

To address the von Neumann bottleneck that leads to both energy and speed degradations, in-memory processing architectures have been proposed as a promising alternative for future computing applications. In this paper, we present an in-memory computing system based on resistive random-access memory (RRAM) crossbar arrays that is reconfigurable and can potentially perform parallel and general computing tasks. The system consists of small look-up tables (LUTs), a memory block, and two search auxiliary blocks, all implemented in the same RRAM crossbar array. External data access and data conversions are eliminated to allow operations fully in-memory. Details of addition, AND logic and multiplication operations are discussed on the basis of search and writeback steps. A compact instruction set consisting of 10 instructions is demonstrated on this architecture through circuit level simulations. Performance evaluations show that the proposed in-memory computing architecture is suitable for handling data-intensive problems. The average power consumption of the crossbar chip is estimated to be 45μW.