Presentation
PixelPrune: Sparse Object Detection for AIoT Systems via In-Sensor Segmentation and Adaptive Data Transfer
DescriptionThis paper addresses two primary challenges in end-to-end object detection within artificial intelligence of things (AIoT) systems: (1) the energy-intensive analog-to-digital converters (ADCs) required for the conversion of analog pixel arrays to digital matrices, and (2) the high data transfers between the sensing unit and computing unit. Our proposed solution involves the implementation of an in-sensor binary segmentation model on analog memristive crossbars to identify the important pixels. Additionally, we propose a data transfer scheme that adaptively selects between dense and sparse data transfer formats based on the sparsity ratio measured from the segmentation mask obtained by the segmentation model. Our results demonstrate that the proposed object detection system achieves significant energy savings along with a considerable 95% reduction in data transfer, all while maintaining high accuracy.
Event Type
Work-in-Progress Poster
TimeTuesday, June 256:00pm - 7:00pm PDT
LocationLevel 2 Lobby
AI
Autonomous Systems
Cloud
Design
EDA
Embedded Systems
IP
Security