Ishan Thakkar, assistant professor in the Department of Electrical and Computer Engineering (ECE), has received a $300,000 grant from the National Science Foundation. Funding will span two years.
The title of the project is "EAGER: Transforming Optical Neural Network Accelerators with Stochastic Computing." J. Todd Hastings, Reese S. Terry Professor and assistant professor Sayed Ahmad Salehi, are Co-PIs on the grant.
Thakkar joined the ECE faculty in 2018.
The abstract for the project is below.
"In the past decade, machine learning algorithms and models such as Deep Neural Networks (DNNs) have become increasingly prevalent for many emerging applications, such as medical prognosis, autonomous transportation, weather forecast, speech recognition and translation, image/video recognition and synthesis. This increasing prevalence has necessitated that the computing hardware platforms that process DNNs deliver consistently high performance and fast processing speeds. This need has driven computer hardware architects to design custom accelerator chips for processing DNNs. As researchers explore more sophisticated and powerful models of DNNs, the compute speed and throughput requirements from these DNN accelerator chips also increase. Traditional electronic implementations of DNN accelerator chips are breaking down under this pressure, which is catastrophic as it prevents the immediate widespread adoption of high-performance artificial intelligence that can transform society and improve lives. Fortunately, silicon photonics-based optical computing (OC) has emerged as an exciting paradigm that can replace slow electronic processing of DNNs with much faster, light-speed DNN processing. By processing DNNs directly in the optical domain, OC-based DNN accelerator chips have the potential to provide up to a thousand times faster processing speeds than traditional electronic chips. However, viable realization and deployment of OC-based DNN accelerators present enormous challenges, because the silicon-photonic building blocks of such accelerators consume very high static power, require excessively large silicon real estate, exhibit susceptibility to uncertainty induced errors, and lack flexibility. This project will involve transformative research to overcome these fundamental challenges and to design groundbreaking architectures of OC-based DNN accelerator chips, which will employ stochastic computing (SC) to realize various DNN processing functions. The merging of SC with OC in a highly synergistic manner will pave the way for realizing future OC-based DNN accelerator chips that are miniature in size but possess the computing power to enable lower-cost, ultra-fast, and highly flexible processing of machine learning and artificial intelligence tasks. Close collaborations with academic partners at the NSF NNCI node, Kentucky Multiscale, will not only aid in the rapid prototyping of the developed accelerator chips but also provide a crucial avenue for technology transfer."
Research reported in this publication was supported by the National Science Foundation under Award Number 2139167. The opinions, findings, and conclusions or recommendations expressed are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.