OpenACMv2: An Accuracy-Constrained Co-Optimization Framework for Approximate DCiM
Researchers have developed OpenACMv2, an open-source framework designed to optimize Digital Compute-in-Memory (DCiM) hardware for neural networks. This framework employs a two-level optimization strategy to balance power, performance, and area (PPA) with accuracy constraints. The first level searches for optimal architecture configurations, while the second refines transistor-level parameters, enabling significant efficiency gains with minimal accuracy loss. AI
IMPACT This framework could lead to more efficient hardware for running AI models, reducing power consumption and improving performance.