IEEE CASS Distinguished Lecture
IEEE CASS Distinguished Lecture
IEEE CASS Distinguished Lecture
Speaker: Dr. Abhronil Sengupta
Title: Opportunities in NeuroAI: A Hardware-Software Co-Design Perspective
Abstract: The massive explosion of foundation models in the past few years has led to widespread
adoption of AI in a plethora of applications. However, the computational resource requirements to enable
these platforms are growing at an unsustainable rate. Further, deep learning systems also suffer from
algorithmic challenges such as the requirement of a huge amount of labelled data, adversarial
susceptibility, lack of interpretability, etc. Brain-inspired neuromorphic computing has the potential to
overcome these concerns of current AI systems. This talk reviews recent developments in the domain of
NeuroAI from my group guided by an overarching system-science perspective. From the top-down
algorithm side, I will delve into methodologies that treat neuromorphic architectures as continuously
evolving dynamical systems, revealing intriguing parallels with the learning dynamics in the brain. Our
developed methodologies enable brain-inspired models to transition beyond simple vision-related tasks to
complex sequence learning problems trained using online local learning. Complementary to this effort, I
will also elaborate on a bottom-up perspective of bridging the computational efficiency gap through the
exploration of novel ferroelectric devices that provide a better match to the computational primitives of AI
systems. Our proposed Compute-In-Memory neuromorphic processors enabled with in-situ learning
functionalities can potentially achieve two to three orders of magnitude energy improvement compared to
state-of-the-art CMOS implementations. From a system implementation standpoint, I will outline several
hardware-software co-design strategies to enable variation-aware, robust, self-healing AI hardware. I will
conclude my talk with my vision of expanding the scope of neuromorphic computing beyond simple
neurons and synapses by forging stronger connections with computational neuroscience, thereby
enabling a new generation of brain-inspired computers.
Speaker Bio: Dr. Abhronil Sengupta is an Associate Professor in the School of Electrical Engineering
and Computer Science at Penn State University. Dr. Sengupta received the PhD degree in Electrical and
Computer Engineering from Purdue University in 2018 and the B.E. degree from Jadavpur University,
India in 2013. He worked as a DAAD (German Academic Exchange Service) Fellow at the University of
Hamburg, Germany in 2012, and as a graduate research intern at Intel Labs in 2016 and Meta Reality
Labs in 2017.
Dr. Sengupta is pursuing an inter-disciplinary research agenda at the intersection of hardware and
software across the stack of sensors, devices, circuits, systems and algorithms for enabling adaptive,
scalable and low-power brain-inspired computing systems. He has published over 100 articles in refereed
journals and conferences and holds 3 US patents. He has received multiple prestigious recognitions,
including the Penn State Engineering Alumni Society (PSEAS) Outstanding Research Award (2026),
invited participant in NAE Grainger Foundation Frontiers of Engineering Symposium (2026), IEEE CEDA
Ernest Kuh Early Career Award (2025), ARO Early Career Award (2024), Purdue Engineering 38 by 38
Award (2024), NSF CAREER Award (2023), IEEE EDS Chenming Hu Early Career Award (2023),
Monkowski Career Development Professorship from Penn State University (2022-2025), IEEE CASS
Outstanding Young Author Award (2019), and Meta Faculty Award (2018). His work on neuromorphic
computing has been highlighted in media by MIT Technology Review, US Department of Defense,
American Institute of Physics, IEEE Spectrum, and Nature Materials. Dr. Sengupta is a Senior Member of
the NAI, IEEE and ACM. He currently serves as an ACM Distinguished Speaker (2024-2027) and IEEE
CASS Distinguished Lecturer (2025-2026). He is also a co-author of the book "Introduction to
Neuromorphic Computing" published by Cambridge University Press.
Location: 8228 Paint Branch Dr, College Park, MD 20742, Zupnik Lecture Hall 1110, Kim Building, University of Maryland, College Park
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Speakers
Dr. Abhronil Sengupta
Opportunities in NeuroAI: A Hardware-Software Co-Design Perspective
The massive explosion of foundation models in the past few years has led to widespread
adoption of AI in a plethora of applications. However, the computational resource requirements to enable
these platforms are growing at an unsustainable rate. Further, deep learning systems also suffer from
algorithmic challenges such as the requirement of a huge amount of labelled data, adversarial
susceptibility, lack of interpretability, etc. Brain-inspired neuromorphic computing has the potential to
overcome these concerns of current AI systems. This talk reviews recent developments in the domain of
NeuroAI from my group guided by an overarching system-science perspective. From the top-down
algorithm side, I will delve into methodologies that treat neuromorphic architectures as continuously
evolving dynamical systems, revealing intriguing parallels with the learning dynamics in the brain. Our
developed methodologies enable brain-inspired models to transition beyond simple vision-related tasks to
complex sequence learning problems trained using online local learning. Complementary to this effort, I
will also elaborate on a bottom-up perspective of bridging the computational efficiency gap through the
exploration of novel ferroelectric devices that provide a better match to the computational primitives of AI
systems. Our proposed Compute-In-Memory neuromorphic processors enabled with in-situ learning
functionalities can potentially achieve two to three orders of magnitude energy improvement compared to
state-of-the-art CMOS implementations. From a system implementation standpoint, I will outline several
hardware-software co-design strategies to enable variation-aware, robust, self-healing AI hardware. I will
conclude my talk with my vision of expanding the scope of neuromorphic computing beyond simple
neurons and synapses by forging stronger connections with computational neuroscience, thereby
enabling a new generation of brain-inspired computers.
Biography:
Dr. Abhronil Sengupta is an Associate Professor in the School of Electrical Engineering
and Computer Science at Penn State University. Dr. Sengupta received the PhD degree in Electrical and
Computer Engineering from Purdue University in 2018 and the B.E. degree from Jadavpur University,
India in 2013. He worked as a DAAD (German Academic Exchange Service) Fellow at the University of
Hamburg, Germany in 2012, and as a graduate research intern at Intel Labs in 2016 and Meta Reality
Labs in 2017.
Dr. Sengupta is pursuing an inter-disciplinary research agenda at the intersection of hardware and
software across the stack of sensors, devices, circuits, systems and algorithms for enabling adaptive,
scalable and low-power brain-inspired computing systems. He has published over 100 articles in refereed
journals and conferences and holds 3 US patents. He has received multiple prestigious recognitions,
including the Penn State Engineering Alumni Society (PSEAS) Outstanding Research Award (2026),
invited participant in NAE Grainger Foundation Frontiers of Engineering Symposium (2026), IEEE CEDA
Ernest Kuh Early Career Award (2025), ARO Early Career Award (2024), Purdue Engineering 38 by 38
Award (2024), NSF CAREER Award (2023), IEEE EDS Chenming Hu Early Career Award (2023),
Monkowski Career Development Professorship from Penn State University (2022-2025), IEEE CASS
Outstanding Young Author Award (2019), and Meta Faculty Award (2018). His work on neuromorphic
computing has been highlighted in media by MIT Technology Review, US Department of Defense,
American Institute of Physics, IEEE Spectrum, and Nature Materials. Dr. Sengupta is a Senior Member of
the NAI, IEEE and ACM. He currently serves as an ACM Distinguished Speaker (2024-2027) and IEEE
CASS Distinguished Lecturer (2025-2026). He is also a co-author of the book "Introduction to
Neuromorphic Computing" published by Cambridge University Press.
Agenda
Seminar by Dr. Abhronil Sengupta
IEEE CASS Distinguished Lecture