AI and ML for embedded systems, Alan Wood (OSHCamp 2017)
Channel: AB Open
Date: 25/06/2018

Artificial Intelligence (AI) and Machine Learning (ML) allow technology to automate what was previously considered unique to human intelligence, we already see this in big data with image classification, speech recognition and sentiment analysis to name just a few applications. How will this effect embedded systems and hardware, what part can open source play in this emerging area by embedding intelligence or intuition into future products.

Alan will provide an overview the current state of machine learning and inference techniques used within embedded applications, he will show how open source software and hardware can be used to apply these ML techniques into embedded and robotics and projects.

Areas covered will include Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), Reinforcement Learning (RL) along with differences between training and inference deployments. Alan will also discuss some emergent AI hardware areas such as energy efficient neuromorphic computation and processing which can perhaps commodify AI over the coming decades.

With both open source software and hardware we are poised to rapidly advance both education, experimentation and development of machine learning into working embedded automation, there could not be a better time to get into this emerging area of technology.

— Alan Wood has been working with parallel distributed programming for several decades. His recent work includes smart grids, 3D printers, robotics, automation and biotec diagnostics. His current research is focused on machine learning for embedded automation using FPGA, CSP and Neural Turing Machines. He is a long term advocate of open source communities, a moderator (aka Folknology) for xCORE, the co-founder of myStorm open hardware FPGA community, as well as a co-founder of Surrey and Hampshire Makerspace.