Modelling, exploration and optimization of hardware accelerators for deep learning applications - Dutt, Arko (Nanyang Technological University, 2023)
Current applications that require processing of large amounts of data, such as in healthcare, transportation, media, banking, telecom, internet-of-things, and security demand for new computing systems with extreme performance and energy efficiency. Several advancements in general-purpose computing (like General Purpose Graphics Processing Unit) and new custom hardware (like Tensor Processing Unit) are proposed to meet the maximum performance needs. These computing systems are still bottle-necked by under-achieved power-efficiency due to excessive data transfers. Though a lot of new computing architectures are emerging in academia and industry targeting efficient processing of application workloads, it takes considerable amount of time to decide on the most efficient hardware solution. Additionally, computer architects cannot guarantee which hardware is the best in terms of maximum performance with least energy consumption. An efficient computing system will require co-optimizations at device-, circuit, architecture- and system-levels. A toolchain or a unified tool that can instantaneously and accurately simulate the hardware costs targeting an application workload, can therefore accelerate the design and optimization of efficient computing systems much before its actual realization.