Lecture
This lecture delves into the optimization strategies for deep learning accelerators, focusing on reducing data movement in DNNs through techniques such as batching, dataflow optimizations, and compression. The instructor explains the trade-offs involved in batching, the benefits of dataflow optimizations for energy efficiency, and the challenges and potential of compression techniques. Various concepts like weight reuse, input reuse, and different dataflow architectures are explored to maximize the efficiency of deep learning accelerators.