Summary
Transferring data between memory and processing units in conventional computing systems is expensive in terms of energy and latency. This data movement consumes significant energy and slows down the processing speed, particularly for data-driven applications such as machine learning workloads. In-memory computing (IMC) is a promising solution to address this issue by performing computations inside memory. However, IMC techniques using emerging memory technologies suffer from multiple key technical challenges that limit their applicability in today’s computing systems. Significant error in the
