Accelerating AI Applications using Analog In-Memory Computing: Challenges and Opportunities

Abstract

Linear transformations are the dominating computation within many artificial intelligence (AI) applications. The natural multiply and accumulate feature of resistive crossbar arrays promise unprecedented processing capabilities to resistive dot-product engines (DPEs), which can accelerate approximate matrix-vector multiplication using analog in-memory computing. Unfortunately, the functional correctness of the accelerated AI applications may be compromised by various sources of errors. In this paper, we will outline the most pressing robustness challenges, the limitations of state-of-The-Art solutions, and future opportunities for research.