Joint Hardware-Workload Co-Optimization for In-Memory Computing Accelerators

Researchers developed a joint hardware-workload co-optimization framework for in-memory computing accelerators that can efficiently support multiple neural network workloads rather than just single specialized models. The framework achieved significant energy-delay-area product reductions of up to 76.2% and 95.5% compared to baseline methods when optimizing across multiple workloads.