Should We Even Optimize for Execution Energy? Rethinking Mapping for MAGIC Design Style

TL;DR

This paper critically examines energy consumption in memristor-based logic-in-memory systems, revealing that initialization energy dominates and suggesting a shift in optimization focus from execution to initialization for better energy efficiency.

Contribution

It provides a detailed energy breakdown of MAGIC-style memristor operations and introduces a more accurate energy estimation method using SPICE simulations.

Findings

Initialization energy is 68x higher than execution energy in MAGIC operations.

Current estimation methods underestimate energy consumption due to coarse-grained techniques.

Focusing on initialization optimization can lead to more significant energy savings.

Abstract

Memristor-based logic-in-memory (LiM) has become popular as a means to overcome the von Neumann bottleneck in traditional data-intensive computing. Recently, the memristor-aided logic (MAGIC) design style has gained immense traction for LiM due to its simplicity. However, understanding the energy distribution during the design of logic operations within the memristive memory is crucial in assessing such an implementation's significance. The current energy estimation methods rely on coarse-grained techniques, which underestimate the energy consumption of MAGIC-styled operations performed on a memristor crossbar. To address this issue, we analyze the energy breakdown in MAGIC operations and propose a solution that utilizes mapping from the SIMPLER MAGIC tool to achieve accurate energy estimation through SPICE simulations. In contrast to existing research that primarily focuses on optimizing execution energy, our findings reveal that the memristor's initialization energy in the MAGIC design style is, on average, 68x higher. We demonstrate that this initialization energy significantly dominates the overall energy consumption. By highlighting this aspect, we aim to redirect the attention of designers towards developing algorithms and strategies that prioritize optimizations in initializations rather than execution for more effective energy savings.