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With the proliferation of networked sensors and artificial intelligence, there is an increasing need for edge computing where data is processed at the sensor level to reduce bandwidth and latency while still preserving energy efficiency. In this talk, I will discuss how meta-optics can be used to implement computation for optical edge sensors, serving to off-load computationally expensive convolutional operations from the digital platform, reducing both latency and power consumption. I will discuss how meta-optics can augment, or replace, conventional imaging optics in achieving parallel optical processing across multiple independent channels for identifying, and classifying, both spatial and spectral features of objects.
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