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

arXiv: https://arxiv.org/abs/2408.03956 · HTML: https://mlsanity.com/paper/2408.03956/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 23 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [CrowdHuman: A Benchmark for Detecting Human in a Crowd](https://mlsanity.com/paper/1805.00123/) (2018)
- [MCUNet: Tiny Deep Learning on IoT Devices](https://mlsanity.com/paper/2007.10319/) (2020)
- [MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning](https://mlsanity.com/paper/2110.15352/) (2021)
- [Vision Meets Drones: A Challenge](https://mlsanity.com/paper/1804.07437/) (2018)
- [TJU-DHD: A Diverse High-Resolution Dataset for Object Detection](https://mlsanity.com/paper/2011.09170/) (2020)
- [POSTER++: A simpler and stronger facial expression recognition network](https://mlsanity.com/paper/2301.12149/) (2023)
- [A Reconfigurable Convolution-in-Pixel CMOS Image Sensor Architecture](https://mlsanity.com/paper/2101.03308/) (2021)

## Built on by (most cited first, 4 of 4)

- [Energy-Efficient & Real-Time Computer Vision with Intelligent Skipping via Reconfigurable CMOS Image Sensors](https://mlsanity.com/paper/2409.17341/) (2024)
- [Region Masking to Accelerate Video Processing on Neuromorphic Hardware](https://mlsanity.com/paper/2503.16775/) (2025)
- [Easz: An Agile Transformer-based Image Compression Framework for Resource-constrained IoTs](https://mlsanity.com/paper/2505.01742/) (2025)
- [GLANCE: Gaze-Led Attention Network for Compressed Edge-inference](https://mlsanity.com/paper/2603.15717/) (2026)

## Abstract (arXiv)

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.