Energy efficient circuits and architectural design for machine learning on edge - Chong, Yi Sheng (Nanyang Technological University, 2023)
The number of Internet of Things (IoT) devices around the world is forecasted to be 50 billion by the year 2025. IoT devices are commonly referred to as edge devices, as they are able to connect to the Internet and operate at the edge of the network. IoT devices are also equipped with sensors to collect data and user input from the working environment. To intelligently process the data, neural network algorithm, a well-known machine learning technique, is employed for high accuracy performance. Since the neural network algorithm is compute and memory intensive, IoT devices face high latency due to their limited computation power on board. Thus, this thesis explores custom circuits and architecture design to accelerate neural network computation on edge devices.