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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 article presents an energy and performance evaluation framework, Eva-CiM, for systems based on CiM architectures. Eva-CiM encompasses a multilevel (from device to architecture) comprehensive tool chain that leverages existing modeling and simulation tools, such as GEM5, McPAT, and DESTINY. 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 the CiM module. Eva-CiM can readily produce energy and performance 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. Eva-CiM enables analyses including the system-level impact of CiM-supported accesses, whether a program is CiM-favorable as well as the pros and cons of increased memory size for CiM. Eva-CiM also facilitates exploration of different design configurations and technologies.

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