Anup Das, PhD
Machine learning methods such as neural networks have been successfully used
in real-time computer vision and signal processing areas. But as demand for
AI grows, the computing and energy resources needed to run this complicated
processing can’t keep up.
Anup Das, PhD, assistant professor of electrical and computer engineering is working with
researchers, including students in the Vertically Integrated Projects
program, on neuromorphic systems, which mimic biological neurons and
synapses and can lessen the computing load.
Das and his research team are working on several federal grants to develop
compiler tool chains to translate a user’s machine learning program to
low-level languages that can be interpreted by neuromorphic systems. A key
initiative is to develop a common representation across different platforms
so that computers across a network can work together to take on smaller
chunks of processing. Resource optimization strategies are being developed
to improve program performance, as well as an Operating System- like
framework that will allow programmers to easily deploy their machine
learning programs on neuromorphic systems. The open-sourced programming
tools will enable faster development and commercialization of neuromorphic
systems in the U.S. and facilitate collaboration with other such communities
worldwide