MemSPICE: Automated Simulation and Energy Estimation Framework for MAGIC-Based Logic-in-Memory

Existing logic-in-memory (LiM) research is limited to generating mappings and micro-operations. In this paper, we present~\emph{MemSPICE}, a novel framework that addresses this gap by automatically generating both the netlist and testbench needed to evaluate the LiM on a memristive crossbar. MemSPICE goes beyond conventional approaches by providing energy estimation scripts to calculate the precise energy consumption of the testbench at the SPICE level. We propose an automated framework that utilizes the mapping obtained from the SIMPLER tool to perform accurate energy estimation through SPICE simulations. To the best of our knowledge, no existing framework is capable of generating a SPICE netlist from a hardware description language. By offering a comprehensive solution for SPICE-based netlist generation, testbench creation, and accurate energy estimation, MemSPICE empowers researchers and engineers working on memristor-based LiM to enhance their understanding and optimization of energy usage in these systems. Finally, we tested the circuits from the ISCAS'85 benchmark on MemSPICE and conducted a detailed energy analysis.Comment: Accepted in ASP-DAC 202

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

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.Comment: Accepted to published in IEEE EMBEDDED SYSTEMS LETTE