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

• Published in: IEEE embedded systems letters Vol. 15; no. 4; pp. 230 - 233
• Main Authors: Singh, Simranjeet, Jha, Chandan Kumar, Bende, Ankit, Thangkhiew, Phrangboklang Lyngton, Rana, Vikas, Patkar, Sachin, Drechsler, Rolf, Merchant, Farhad
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Benchmark testing; Computer memory; Design; Digital logic-in-memory(LiM); Energy consumption; Energy distribution; energy efficiency; Estimation; Logic; Logic gates; Mapping; memristor-aided logic (MAGIC); Memristors; Optimization; SPICE; Writing
• ISSN: 1943-0663; 1943-0671
• DOI: 10.1109/LES.2023.3298740

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, <inline-formula> <tex-math notation="LaTeX">68\times </tex-math></inline-formula> 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 toward developing algorithms and strategies that prioritize optimizations in initializations rather than execution for more effective energy savings.