Machine Learning Accelerators: Types and Efficiency

This lecture covers the types of machine learning workloads, including inference and training, and the computational intensity of deep neural networks. It discusses the different types of DNN layers, such as convolutional and fully connected layers, and the computational differences between them. The lecture also explores the concept of systolic arrays in DNNs, focusing on spatially distributed processing elements and the core operation of matrix-matrix multiplication. Additionally, it delves into the inefficiencies of CPUs and GPUs for DNNs, highlighting the need for specialized accelerators like TPUs. The instructor emphasizes the importance of data movement in DNNs and how systems like TPUs leverage algorithm tolerance to low precision to achieve high performance at low utilization.