Dr. Anup Das
Dr. Anup Das, Assistant Professor in Drexel's Electrical and Computer Engineering Department, has been selected by the National Science Foundation (NSF) to receive a Faculty Early Career Development Award (CAREER). The CAREER Award is arguably the most distinguished recognition a faculty member can receive early in their career.
The project, entitled "Facilitating Dependable Neuromorphic Computing: Vision, Architecture, and Impact on Programmability," is funded for five years at a total of $540,240.
"A grand challenge for the neuromophic computing community...[is] how to tolerate machine learning errors due to hardware faults," says Dr. Anup Das. "This is critical considering that machine learning is fundamental to sustaining the economic growth in many sectors within the U.S. science and engineering enterprises. Therefore, incorrect behavior due to hardware faults can severely limit the dependability of machine learning, impeding their technological progress. This CAREER project will address the dependability challenge of neuromorphic computing through hardware-software co-design and design-technology co-optimization."
"As an integral part of the dependability research," continues Dr. Das, "this project will educate students and professors on how to improve error tolerance of machine learning and teach them out to program and use neuromorphic hardware to execute them."
The College of Engineering and the ECE Department extend a hearty congratulations to Dr. Das on this great achievement!
The full text of Dr. Das's project abstract can be found below:
Machine learning is the key to sustaining the economic growth in many sectors within the U.S. science and engineering enterprise. Neuromorphic architectures that mimic biological neurons and synapses can execute machine learning algorithms in an energy-efficient manner, allowing for their integration with the internet-of-things, another industry sector projected to have an economic impact of more than $1 trillion by 2025. However, neuromorphic architectures are fundamentally unreliable. They introduce errors during execution, limiting the dependability of machine learning and impeding their technological progress. This project will address dependability challenges of neuromorphic computing, by looking at all levels, from building error-resilient machine learning algorithms to designing fault-tolerant hardware. This project will advance the field by 1) making neuromorphic computing reliable, efficient, programmable, and easy-to-use for the community, 2) teaching future science and engineering students how to make machine learning algorithms error-resilient, 3) creating job opportunities in neuromorphic industry through national and international internships and collaborations, 4) raising interest of high school students in STEM through neuromorphic computing enabled robotics workshops, and 5) building an integrated neuromorphic community through a new focused conference on neuromorphic computing.
This project poses broad research questions with far-reaching implications in dependable neuromorphic computing: what are the reliability issues in neuromorphic architectures and how to model them?; how do these reliability issues manifest as errors and impact the performance of machine learning algorithms?; how to improve error tolerance in these algorithms by exploiting error resilience and self-repair properties in the brain?; and how to proactively mitigate reliability issues in neuromorphic architectures to avoid errors in the first place? This project seeks to answer these research questions through the following three key research activities: 1) embedding biological self-repair properties in machine learning algorithms; 2) designing fault-tolerant hardware to implement these algorithms; and 3) proactively mitigating reliability issues and facilitating fault tolerance in hardware through algorithm/architecture co-design and design/technology co-optimization. These research activities will be tightly integrated into teaching. As new error resilience and self-repair properties in the brain are investigated, students will be taught how to use them in building applications, and to analyze their reliability impact in hardware. The educational goal is developing curriculum at Drexel University that will teach students how to design error-tolerant machine learning applications and compile them for neuromorphic architectures to improve system programmability and reliability. Throughout this project, robot workshops will be organized annually for Drexel's Eureka (STEM for girls) and Philadelphia's high school students, jointly with the City of Philadelphia, to raise this community's interest in STEM. The project will also recruit undergraduate and graduate students in research and outreach activities, with emphasis on female and minority students.