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Neuromorphic Computing: Bridging the gap between Nanoelectronics, Neuroscience and Machine Learning

Neuromorphic Computing: Bridging the gap between Nanoelectronics, Neuroscience and Machine Learning

Speaker: Abhronil Sengupta – University Park, PA, United States
Topic(s): Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing , Hardware, Power and Energy

Abstract

While research in designing brain-inspired algorithms have attained a stage where such Artificial Intelligence platforms are being able to outperform humans at several cognitive tasks, an often-unnoticed cost is the huge computational expenses required for running these algorithms in hardware. Recent explorations have also revealed several algorithmic vulnerabilities of deep learning systems like adversarial susceptibility, lack of explainability, catastrophic forgetting, to name a few. Bridging the computational and algorithmic efficiency gap necessitates the exploration of hardware and algorithms that provide a better match to the computational primitives of biological processing – neurons and synapses, and which require a significant rethinking of traditional von-Neumann based computing. This talk reviews recent developments in the domain of neuromorphic computing paradigms from an overarching system science perspective with an end-to-end co-design focus from computational neuroscience and machine learning to hardware and applications. Such neuromorphic systems can potentially provide significantly lower computational overhead in contrast to standard deep learning platforms, especially in sparse, event-driven application domains with temporal information processing.

About this Lecture

Number of Slides: 40
Duration: 60 minutes
Languages Available: English
Last Updated:

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