Rethinking Deep Learning on Remote Smart Sensors
Neural networks for deep learning have been proven successful for many different domains, such as autonomous driving, conversational agents, autonomous robotics and computer vision. Neural network models are typically trained and executed on GPUs, but these have significant energy costs and lack portability needed for remote smart devices. FPGAs and embedded GPUs solve this problem, but cannot host large trained models. Thus, mechanisms to compress neural networks are needed to fit within hardware resource constraints without losing accuracy of AI inferences the model can make.
This PhD topic will combine hardware programming (embedded GPUs and programmable FPGAs) and neural network transformation approaches.
It is a very timely topic, with neural network approximation projects being led by industry: Distiller (Intel), Tensorflow Lite (Google) and FINN (Xilinx), which signifies the move away from focusing solely on high performance AI, towards so-called Edge Computing I.e. ubiquitous low powered AI sensors deployed from homes and vehicles, to robots and factories.