Session

DescriptionThis session presents state-of-the-art work in architecture design focusing on optimization techniques such as quantization, sparsity, pruning, and compression for DNN accelerators. The session begins with a series of presentations on quantization, which is an increasingly popular and energy efficient technique used for deep neural networks (DNNs). The session presents other hot topic techniques such as utilization and optimizing sparsity and pruning, with a focus on the ever-popular transformer attention architecture.