Memory-Aware ASIC Design for AI Workloads
Understand memory-aware ASIC design for AI workloads, from memory hierarchy and dataflow to NoC, DMA, and hardware-software co-design. Every AI accelerator is ultimately constrained by two resources: computation and data movement. In recent times, AI processors have delivered remarkable gains in compute density through dedicated neural processing units (NPUs), larger Multiply-Accumulate (MAC) arrays, and increasingly parallel architectures. However, as AI workloads have evolved from convolutional neural networks to transformer-based and multimodal models, memory has emerged as the limiting factor. In many AI ASIC designs, the processing engine can execute far more operations than the memory subsystem can sustain.