Event-based sensing and computing for efficient edge artificial intelligence and TinyML applications
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.
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.
Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning
Authors: Abdullah Abuolaim (York University)*; Mahmoud Afifi (Apple); Michael S Brown (York University)
Many camera sensors use a dual-pixel (DP) design that operates as a rudimentary light field providing two sub-aperture views of a scene in a single capture. The DP sensor was developed to improve how cameras perform autofocus. Since the DP sensor's introduction, researchers have found additional uses for the DP data, such as depth estimation, reflection removal, and defocus deblurring. We are interested in the latter task of defocus deblurring. In particular, we propose a single-image deblurring network that incorporates the two sub-aperture views into a multi-task framework. Specifically, we show that jointly learning to predict the two DP views from a single blurry input image improves the network's ability to learn to deblur the image. Our experiments show this multi-task strategy achieves +1dB PSNR improvement over state-of-the-art defocus deblurring methods. In addition, our multi-task framework allows accurate DP-view synthesis (e.g., ~39dB PSNR) from the single input image. These high-quality DP views can be used for other DP-based applications, such as reflection removal. As part of this effort, we have captured a new dataset of 7,059 high-quality images to support our training for the DP-view synthesis task.
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