An Exploration of the Impact of Mapping Style and Device Roadmap on Simulated ReRAM Architectures for Neuromorphic Computing

TL;DR

This study evaluates how different mapping styles and device roadmaps affect ReRAM-based neuromorphic systems, showing that high-performance devices and novel mappings significantly improve latency and energy efficiency.

Contribution

It provides new insights into optimizing ReRAM neuromorphic architectures by analyzing the effects of mapping techniques and device roadmaps through detailed simulations.

Findings

High-performance devices reduce latency more than energy costs.

Novel mapping techniques cut latency by nearly 30%.

Optimizing mapping style and device roadmap enhances system efficiency.

Abstract

This paper investigates the relationship between mapping style and device roadmap in Resistive Random Access Memory (ReRAM) architectures for neuromorphic computing. The study leverages simulations using DNN+NeuroSim to evaluate the impact of different parameters on chip performance, including latency, energy consumption, and overall system efficiency. The results demonstrate that novel mapping techniques and a high-performance (HP) device roadmap are optimal if energy and speed considerations are weighted equally. This is because as the study demonstrates, HP devices provide a latency cut that outsizes the energy cost. Additionally, adopting novel mapping in the device cuts latency by nearly 30% while being slightly more energy efficient. The findings highlight the importance of considering mapping style and device roadmap in optimizing ReRAM architectures for neuromorphic computing, which may contribute to advancing the practical implementation of ReRAM in computational systems.