# HiRISE: High-Resolution Image Scaling for Edge ML via In-Sensor Compression and Selective ROI Source: [HiRISE: High-Resolution Image Scaling for Edge ML via In-Sensor Compression and Selective ROI]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:08:39.928Z Authors: Brendan Reidy, Sepehr Tabrizchi, Mohamadreza Mohammadi, Shaahin Angizi, Arman Roohi, Ramtin Zand Published: 2024-07-23T16:26:05.000Z ## 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 17.7x reduction in data transfer and energy consumption. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()