Modeling and Simulation Frameworks for Processing-in-Memory Architectures

TL;DR

This paper reviews various simulation frameworks for processing-in-memory architectures, emphasizing their roles, differences, and challenges to aid researchers in selecting appropriate tools for PIM design evaluation.

Contribution

It provides a comprehensive categorization and comparison of PIM simulation tools, benchmark suites, and discusses open challenges to advance PIM research methodology.

Findings

Survey of diverse PIM simulators and their features

Analysis of trade-offs in simulation fidelity and scalability

Identification of open challenges in PIM simulation methods

Abstract

Processing-in-Memory (PIM) has emerged as a promising computing paradigm to address the memory wall and the fundamental bottleneck of the von Neumann architecture by reducing costly data movement between memory and processing units. As with any engineering challenge, identifying the most effective solutions requires thorough exploration of diverse architectural proposals, device technologies, and application domains. In this context, simulation plays a critical role in enabling researchers to evaluate, compare, and refine PIM designs prior to fabrication. Over the past decade, a variety of PIM simulators have been introduced, spanning low-level device models, architectural frameworks, and application-oriented environments. These tools differ significantly in fidelity, scalability, supported memory/compute technologies, and benchmark compatibility. Understanding these trade-offs is essential for researchers to select appropriate simulators that accurately map and validate their research efforts. This chapter provides a comprehensive overview of PIM simulation methodologies and tools. We categorize simulators according to abstraction levels, design objectives, and evaluation metrics, highlighting representative examples. To improve accessibility, some content may appear in multiple contexts to guide readers with different backgrounds. We also survey benchmark suites commonly employed in PIM studies and discuss open challenges in simulation methodology, paving the way for more reliable, scalable, and efficient PIM modeling.