Memory-Side Acceleration and Sparse Compression for Quantized Packed Convolutions

Weaver, Alex; Kavi, Krishna M.; Vasireddy, Pranathi; Mehta, Gayatri

doi:10.1109/sbac-pad55451.2022.00019

Cited by 3 publications

(1 citation statement)

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“…These automation techniques may also be applied on a future prototype with the trade off of a small latency in the runtime of the system. Preprocessing as shown in Figure 1.4 can make the computation process optimized when certain pixel values such as white #ffffff with an RGB value of 255 is stored in a sparse vector context [68]. A centered crop was invoked on the command line before a background removal was processed.…”

Section: Datasetmentioning

confidence: 99%

Lite-Agro: Integrating Federated Learning and TinyML on IoAT-Edge for Plant Disease Classification

Dockendorf

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Lite-Agro studies applications of TinyML in pear (Pyrus communis) tree disease identification and explores hardware implementations with an ESP32 microcontroller. The study works with the DiaMOS Pear Dataset to learn through image analysis whether the leaf is healthy or not, and classifies it according to curl, healthy, spot or slug categories. The system is designed as a low cost and light-duty computing detection edge solution that compares models such as InceptionV3, XceptionV3, EfficientNetB0, and MobileNetV2. This work also researches integration with federated learning frameworks and provides an introduction to federated averaging algorithms.

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Section: Datasetmentioning

confidence: 99%