# Eva-CiM: A System-Level Performance and Energy Evaluation Framework for Computing-in-Memory Architectures

## TL;DR

Eva-CiM is a comprehensive system-level framework that evaluates the performance and energy efficiency of computing-in-memory architectures, aiding design choices and technology assessments.

## Contribution

It introduces a multi-level modeling tool chain with novel analysis methods for accurate energy prediction in CiM systems, supporting rapid design space exploration.

## Key findings

- Eva-CiM achieves 1.3-6.0X energy savings with SRAM.
- It shows 2.0-7.9X energy improvements with FeFET-RAM.
- The framework enables analysis of CiM's impact on system performance.

## Abstract

Computing-in-Memory (CiM) architectures aim to reduce costly data transfers by performing arithmetic and logic operations in memory and hence relieve the pressure due to the memory wall. However, determining whether a given workload can really benefit from CiM, which memory hierarchy and what device technology should be adopted by a CiM architecture requires in-depth study that is not only time consuming but also demands significant expertise in architectures and compilers. This paper presents an energy evaluation framework, Eva-CiM, for systems based on CiM architectures. Eva-CiM encompasses a multi-level (from device to architecture) comprehensive tool chain by leveraging existing modeling and simulation tools such as GEM5, McPAT [2] and DESTINY [3]. To support high-confidence prediction, rapid design space exploration and ease of use, Eva-CiM introduces several novel modeling/analysis approaches including models for capturing memory access and dependency-aware ISA traces, and for quantifying interactions between the host CPU and CiM modules. Eva-CiM can readily produce energy estimates of the entire system for a given program, a processor architecture, and the CiM array and technology specifications. Eva-CiM is validated by comparing with DESTINY [3] and [4], and enables findings including practical contributions from CiM-supported accesses, CiM-sensitive benchmarking as well as the pros and cons of increased memory size for CiM. Eva-CiM also enables exploration over different configurations and device technologies, showing 1.3-6.0X energy improvement for SRAM and 2.0-7.9X for FeFET-RAM, respectively.