Brain-Inspired Spatiotemporal Processing Algorithms for Efficient Event-Based Perception

Neuromorphic event-based cameras can unlock the true potential of bio-plausible sensing systems that mimic our human perception. However, efficient spatiotemporal processing algorithms must enable their low-power, low-latency, real-world application. In this talk, we highlight our recent efforts in...

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Bibliographic Details
Main Authors: Chakraborty, Biswadeep, Kamal, Uday, She, Xueyuan, Dash, Saurabh, Mukhopadhyay, Saibal
Format: Conference Proceeding
Language:English
Subjects:
Approximation algorithms
Associative memory
brain-inspired
efficient
event-based camera
Inference algorithms
Neuromorphics
Spatiotemporal phenomena
spiking neural network
Throughput
Training
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Description
Summary:Neuromorphic event-based cameras can unlock the true potential of bio-plausible sensing systems that mimic our human perception. However, efficient spatiotemporal processing algorithms must enable their low-power, low-latency, real-world application. In this talk, we highlight our recent efforts in this direction. Specifically, we talk about how brain-inspired algorithms such as spiking neural networks (SNNs) can approximate spatiotemporal sequences efficiently without requiring complex recurrent structures. Next, we discuss their event-driven formulation for training and inference that can achieve realtime throughput on existing commercial hardware. We also show how a brain-inspired recurrent SNN can be modeled to perform on event-camera data. Finally, we will talk about the potential application of associative memory structures to efficiently build representation for event-based perception.
ISSN:1558-1101
DOI:10.23919/DATE56975.2023.10136914