HiRISE: High-Resolution Image Scaling for Edge ML via In-Sensor Compression and Selective ROI

Document Type

Conference Proceeding

Publication Date

11-7-2024

Abstract

With the rise of tiny IoT devices powered by machine learning (ML), many researchers have directed their focus toward compressing models to fit on tiny edge devices. Recent works have achieved remarkable success in compressing ML models for object detection and image classification on microcontrollers with small memory, e.g., 512kB SRAM. However, there remain many challenges prohibiting the deployment of ML systems that require high-resolution images. Due to fundamental limits in memory capacity for tiny IoT devices, it may be physically impossible to store large images without external hardware. To this end, we propose a high-resolution image scaling system for edge ML, called HiRISE, which is equipped with selective region-of-interest (ROI) capability leveraging analog in-sensor image scaling. Our methodology not only significantly reduces the peak memory requirements, but also achieves up to 17.7× reduction in data transfer and energy consumption.

Identifier

85205860688 (Scopus)

ISBN

[9798400706011]

Publication Title

Proceedings - Design Automation Conference

External Full Text Location

https://doi.org/10.1145/3649329.3656539

ISSN

0738100X

Grant

2340249

Fund Ref

National Science Foundation

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