Summary
James Davis is a Research Fellow in the Department of Electrical and Electronic Engineering's Circuits and Systems group at Imperial College London.
You can find James on Google Scholar and connect with him on LinkedIn.
His research interests include:
- Runtime monitoring of digital electronic hardware
- Computer arithmetic
- Neural network inference
- Fault tolerance, reliability and lifetime extension
- Self-adaptive systems
- Reconfigurable computing
- Heterogeneous and embedded systems.
James serves on the technical programme committees of the four top-tier reconfigurable computing conferences (FPGA, FCCM, FPL and FPT) and is a multi-best paper award recipient. He is a Member of the IEEE and ACM.
Publications
Journals
Li H, McInerney I, Davis J, et al., 2021, Digit Stability Inference for Iterative Methods Using Redundant Number Representation, Ieee Transactions on Computers, Vol:70, ISSN:0018-9340, Pages:1074-1080
Conference
Wang E, Davis J, Stavrou G-I, et al., Logic Shrinkage: Learned FPGA Netlist Sparsity for Efficient Neural Network Inference, ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, ACM
Wang E, Davis J, Moro D, et al., 2021, Enabling Binary Neural Network Training on the Edge, International Workshop on Embedded and Mobile Deep Learning (EDML), ACM, Pages:37-38
Wang E, Davis J, Moro D, et al., Enabling Binary Neural Network Training on the Edge, Workshop on Binary Networks for Computer Vision