Optimizing Weight Mapping and Data Flow for Convolutional Neural Networks on RRAM Based Processing-In-Memory Architecture

Abstract

Resistive random access memory (RRAM) based array architecture has been proposed for on-chip acceleration of convolutional neural networks (CNNs), where the array could be configured for dot-product computation in a parallel fashion by summing up the column currents. Prior processing-in-memory (PIM) designs unroll each 3D kernel of the convolutional layers into a vertical column of a large weight matrix, where the input data will be accessed multiple times. As a result, significant latency and energy are consumed in interconnect and buffer. In this paper, in order to maximize both weight and input data reuse for RRAM based PIM architecture, we propose a novel weight mapping method and the corresponding data flow which divides the kernels and assign the input data into different processing-elements (PEs) according to their spatial locations. The proposed design achieves ∼65% save in latency and energy for interconnect and buffer, and yields overall 2.1× speed up and ∼17% improvement in the energy efficiency in terms of TOPS/W for VGG-16 CNN, compared with the prior design based on the conventional mapping method.