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Computing at the extreme edge allows systems with high-resolution sensors to be pushed well outside the reach of traditional communication and power delivery, requiring high-performance, high-energy-efficiency architectures to run complex ML, DSP, image processing, etc. Recent work has demonstrated the suitability of CGRAs for energy-minimal computation, but has focused strictly on energy optimization, neglecting performance. Pipestitch is an energy-minimal CGRA architecture that adds lightweight hardware threads to ordered dataflow, exploiting abundant, untapped parallelism in the complex workloads needed to meet the demands of emerging sensing applications. Pipestitch introduces a programming model, control-flow operator, and synchronization network to allow lightweight hardware threads to pipeline on the CGRA fabric. Across 5 important sparse workloads, Pipestitch achieves a 3.49 × increase in performance over RipTide, the state-of-the-art, at a cost of a 1.10 × increase in area and a 1.05 × increase in energy. 

Batteryless image sensors present an opportunity for long-life, long-range sensor deployments that require zero maintenance, and have low cost. Such deployments are critical for enabling remote sensing applications, e.g., instrumenting national highways, where individual devices are deployed far (kms away) from supporting infrastructure. In this work, we develop and characterize Camaroptera, the first batteryless image-sensing platform to combine energy-harvesting with active, long-range (LoRa) communication. We also equip Camaroptera with a Machine Learning-based processing pipeline to mitigate costly, long-distance communication of image data. This processing pipeline filters out uninteresting images and only transmits the images interesting to the application. We show that compared to running a traditional Sense-and-Send workload, Camaroptera’s Local Inference pipeline captures and sends upto \( 12\times \) more images of interest to an application. By performing Local Inference , Camaroptera also sends upto \( 6.5\times \) fewer uninteresting images, instead using that energy to capture upto \( 14.7\times \) more new images, increasing its sensing effectiveness and availability. We fully prototype the Camaroptera hardware platform in a compact, 2 cm \( \times \) 3 cm \( \times \) 5 cm volume. Our evaluation demonstrates the viability of a batteryless, remote, visual-sensing platform in a small package that collects and usefully processes acquired data and transmits it over long distances (kms), while being deployed for multiple decades with zero maintenance.