My current research focuses on:
· Using what we’ve learned about parallel and distributed computing to speed up the training of Deep Neural Nets – e.g. FireCaffe
· Using what we’ve learned about embedded computing to design and implement fast, accurate, energy-efficient neural nets for computer vison problems – e.g. SqueezeNet, SqueezeDet, SqueezeSeg, SqueezeNext etc.
· Using what we’ve learned about mapping Deep Neural Nets to embedded hardware to explore the co-design of DNNs and NN accelerators – e.g. the Squeezelerator
I currently have post-doctoral research positions in each of these areas. Please contact me if you’re interested.
Past Research Projects
Evaluating the impact of deep submicron process geometries on computer-aided design of integrated circuits using Berkeley Advanced Chip Performance Calculator (BACPAC).
Compilation of software for popular embedded processors - especially DSP's (eg. SPAM).