Current Research

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

Par Lab

Exploring Design Patterns for Parallel Computing

Modern Embedded Systems Compilers Architectures and Languages (MESCAL)

Closing the performance gap between ASIC and custom designs

Closing the power gap between ASIC and custom designs

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).