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Sense, Predict, Adapt, Repeat: A Blueprint for Design of New Adaptive AI-Centric Sensing Systems
As Moore's Law loses momentum, improving size, performance, and efficiency of
processors has become increasingly challenging, ending the era of predictable
improvements in hardware performance. Meanwhile, the widespread incorporation
of high-definition sensors in consumer devices and autonomous technologies has
fueled a significant upsurge in sensory data. Current global trends reveal that
the volume of generated data already exceeds human consumption capacity, making
AI algorithms the primary consumers of data worldwide. To address this, a novel
approach to designing AI-centric sensing systems is needed that can bridge the
gap between the increasing capabilities of high-definition sensors and the
limitations of AI processors. This paper provides an overview of efficient
sensing and perception methods in both AI and sensing domains, emphasizing the
necessity of co-designing AI algorithms and sensing systems for dynamic
perception. The proposed approach involves a framework for designing and
analyzing dynamic AI-in-the-loop sensing systems, suggesting a fundamentally
new method for designing adaptive sensing systems through inference-time
AI-to-sensor feedback and end-to-end efficiency and performance optimization
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