On-Chip Memory Technology Design Space Explorations for Mobile Deep Neural Network Accelerators

Abstract

Deep neural network (DNN) inference tasks have become ubiquitous workloads on mobile SoCs and demand energy-efficient hardware accelerators. Mobile DNN accelerators are heavily area-constrained, with only minimal on-chip SRAM, which results in heavy use of inefficient off-chip DRAM. With diminishing returns from conventional silicon technology scaling, emerging memory technologies that offer better area density than SRAM can boost accelerator efficiency by minimizing costly off-chip DRAM accesses. This paper presents a detailed design space exploration (DSE) of technology-system co-design for systolic-array accelerators. We focus on practical/mature on-chip memory technologies, including SRAM, eDRAM, MRAM, and 3D vertical RRAM (VRRAM). The DSE employs state-of-the-art optimizations (e.g., model compression and optimized buffer scheduling), and evaluates results on important models including ResNet-50, MobileNet, and Faster-RCNN. Compared to an SRAM/DRAM baseline, MRAM-based accelerators show up to 4.68× energy benefits (57% area overhead), while a 3D VRRAM-based design achieves 2.22 × energy benefits (33% area reduction).