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Event Series Event Series: INRC Spring Forum

INRC Forum: Kenneth Stewart

6 June, 2023 @ 08:00 - 09:00 PDT

Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing

Abstract:Achieving real-time, personalized intelligence at the edge with learning capabilities holds enormous promise to enhance our daily experiences and assist in decision-making, planning, and sensing. Yet, today’s technology encounters difficulties with efficient and reliable learning at the edge, due to a lack of personalized data, insufficient hardware, and the inherent challenges posed by online learning. Over time and across multiple developmental phases, the brain has evolved to incorporate new knowledge by efficiently building on previous knowledge. We seek to emulate this remarkable process in digital neuromorphic technology through two interconnected stages of learning.
Initially, a meta-training phase fine-tunes the learning hardware’s hyperparameters for few-shot learning by deploying a differentiable simulation of three-factor learning in a neuromorphic chip. This meta-training process refines the synaptic plasticity and related hyperparameters to align with the specific dynamics inherent in the hardware and the given task domain. During the subsequent deployment stage, these optimized hyperparameters enable accurate learning of new classes using the local three-factor synaptic plasticity updates.
We demonstrate our approach using event-driven vision sensor data and the Intel Loihi neuromorphic processor and the associated plasticity dynamics, achieving state-of-the-art accuracy in learning new categories in one-shot in real-time among three task domains. Our methodology is versatile and can be applied to situations demanding quick learning and adaptation at the edge, such as navigating unfamiliar environments or learning unexpected categories of data through user engagement.

Bio: Kenneth Stewart is a final year Ph.D. candidate in Computer Science at the University of California, Irvine advised by professors Emre Neftci, Nikil Dutt, and Jeffery Krichmar. Throughout his Ph.D. Kenneth has investigated adaptive learning algorithms with Spiking Neural Networks that can be applied in Neuromorphic hardware for online, on-chip learning. During his Ph.D. Kenneth has published several papers in the area and was a candidate for the IEEE AICAS’20 best paper award. In addition to papers, Kenneth co-authored patents regarding adaptive edge learning for gesture and speech recognition applications with the Accenture Future Tech Lab. Kenneth is one of the leading members of Neurobench’s Few-shot Online Learning initiative trying to motivate further research into the area. After earning his degree at the end of the Summer Kenneth hopes to scale up his research to apply it to real-world problems.

For the meeting link, see the full INRC Forum Spring 2023 Schedule (accessible only to INRC Affiliates and Fully Engaged Members).