Image Sensors World Go to the original article...
Federico CORRADI, Senior Neuromorphic Researcher, IMEC
The advent of neuro-inspired computing represents a paradigm shift for edge Artificial Intelligence (AI) and TinyML applications. Neurocomputing principles enable the development of neuromorphic systems with strict energy and cost reduction constraints for signal processing applications at the edge. In these applications, the system needs to accurately respond to the data sensed in real-time, with low power, directly in the physical world, and without resorting to cloud-based computing resources.
In this talk, I will introduce key concepts underpinning our research: on-demand computing, sparsity, time-series processing, event-based sensory fusion, and learning. I will then showcase some examples of a new sensing and computing hardware generation that employs these neuro-inspired fundamental principles for achieving efficient and accurate TinyML applications. Specifically, I will present novel computer architectures and event-based sensing systems that employ spiking neural networks with specialized analog and digital circuits. These systems use an entirely different model of computation than our standard computers. Instead of relying upon software stored in memory and fast central processing units, they exploit real-time physical interactions among neurons and synapses and communicate using binary pulses (i.e., spikes). Furthermore, unlike software models, our specialized hardware circuits consume low power and naturally perform on-demand computing only when input stimuli are present. These advancements offer a route toward TinyML systems composed of neuromorphic computing devices for real-world applications.
Authors: Abdullah Abuolaim (York University)*; Mahmoud Afifi (Apple); Michael S Brown (York University)