Seetharami Seelam, Apoorve Mohan, et al.
ISCA 2023
This brief presents an overview of recent tools and research efforts aimed at enhancing the programmability and reliability of In-Memory Computing (IMC)-based systems. We discuss hardware-aware training techniques that improve model resilience to analog device imperfections, and explore mapping strategies that balance accuracy and performance for heterogeneous IMC-based accelerators. Additionally, we examine a compiler framework that abstracts hardware complexities and enables seamless integration of these accelerators into existing deployment pipelines. By combining these approaches with advanced simulation tools, we propose an end-to-end workflow that facilitates the practical deployment and optimization of IMC technologies across diverse memory types and architectural designs.
Seetharami Seelam, Apoorve Mohan, et al.
ISCA 2023
Pooja Aggarwal, Ajay Gupta, et al.
ICSOC 2020
Pratik Mishra, Caner Gözübüyük, et al.
IAAI 2026
David Wolpert, Gerry Strevig, et al.
ISSCC 2025