Designers of neural network accelerator (NNA) IP have a Herculean task on their hands: making sure that their product is sufficiently general to apply to a very wide range of current and future applications, whilst guaranteeing high performance. In the mobile, automotive, data centre and embedded spaces targeted by Imagination’s cutting-edge IMG Series4 NNAs, there even more stringent constraints on bandwidth, area and power consumption. The engineers at Imagination have found innovative ways to address these daunting challenges and deliver ultra-high-performance and future-proof IP.
James is a member of Imagination’s AI Research team, where he works primarily on neural network accelerators, compilers and low-precision inference targeting embedded systems. In his time as a researcher, he has accumulated 24 granted patents and has contributed to publications in international computer vision conferences including ECCV and ICPR. In 2020 he received Electronics Weekly’s BrightSparks award for young engineers in recognition of his research and his work promoting STEM subjects to secondary school pupils in the UK. His research interests include image processing, ray tracing, machine learning and computer vision. He undertook his PhD studies at the University of Surrey’s Centre for Vision, Speech and Signal Processing (CVSSP) on shape-assisted intrinsic image decomposition and holds a BEng from the University of Southampton in Electronic Engineering.