  Hyperspectral cameras generate a large amount of data due to the presence of
hundreds of spectral bands as opposed to only three channels (red, green, and
blue) in traditional cameras. This requires a significant amount of data
transmission between the hyperspectral image sensor and a processor used to
classify/detect/track the images, frame by frame, expending high energy and
causing bandwidth and security bottlenecks. To mitigate this problem, we
propose a form of processing-in-pixel (PIP) that leverages advanced CMOS
technologies to enable the pixel array to perform a wide range of complex
operations required by the modern convolutional neural networks (CNN) for
hyperspectral image recognition (HSI). Consequently, our PIP-optimized custom
CNN layers effectively compress the input data, significantly reducing the
bandwidth required to transmit the data downstream to the HSI processing unit.
This reduces the average energy consumption associated with pixel array of
cameras and the CNN processing unit by 25.06x and 3.90x respectively, compared
to existing hardware implementations. Our custom models yield average test
accuracies within 0.56% of the baseline models for the standard HSI benchmarks.
