# Reaching the Edge of the Edge: Image Analysis in Space

arXiv: https://arxiv.org/abs/2301.04954 · HTML: https://mlsanity.com/paper/2301.04954/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 78 references that are in the map.
- First posted on arXiv 12 Jan 2023.

## Builds on (most cited first, 20 of 20)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [FINN: A Framework for Fast, Scalable Binarized Neural Network Inference](https://mlsanity.com/paper/1612.07119/) (2016)
- [Fast inference of deep neural networks in FPGAs for particle physics](https://mlsanity.com/paper/1804.06913/) (2018)
- [MLPerf Tiny Benchmark](https://mlsanity.com/paper/2106.07597/) (2021)
- [CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs](https://mlsanity.com/paper/1801.06601/) (2018)
- [Benchmarking TinyML Systems: Challenges and Direction](https://mlsanity.com/paper/2003.04821/) (2020)
- [PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning](https://mlsanity.com/paper/2001.00138/) (2020)
- [LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applications](https://mlsanity.com/paper/2004.03021/) (2020)
- [Memory-Driven Mixed Low Precision Quantization For Enabling Deep Network Inference On Microcontrollers](https://mlsanity.com/paper/1905.13082/) (2019)
- [SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems](https://mlsanity.com/paper/1909.09709/) (2019)
- [Larq Compute Engine: Design, Benchmark, and Deploy State-of-the-Art Binarized Neural Networks](https://mlsanity.com/paper/2011.09398/) (2020)
- [MLPerf Mobile Inference Benchmark](https://mlsanity.com/paper/2012.02328/) (2020)
- [Accelerating Deep Learning Applications in Space](https://mlsanity.com/paper/2007.11089/) (2020)
- [PoET-BiN: Power Efficient Tiny Binary Neurons](https://mlsanity.com/paper/2002.09794/) (2020)
- [The NebulaStream Platform: Data and Application Management for the Internet of Things](https://mlsanity.com/paper/1910.07867/) (2019)
- [Being-ahead: Benchmarking and Exploring Accelerators for Hardware-Efficient AI Deployment](https://mlsanity.com/paper/2104.02251/) (2021)
- [Satellite Image Search in AgoraEO](https://mlsanity.com/paper/2208.10830/) (2022)

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

- [TinyML Enhances CubeSat Mission Capabilities](https://mlsanity.com/paper/2603.20174/) (2026)
- [Position: Neglecting the Sustainability of AI is Fuelling a Global AI Arms Race](https://mlsanity.com/paper/2502.20016/) (2025)
- [FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review](https://mlsanity.com/paper/2506.03938/) (2025)
- [Adaptive and Robust Image Processing on CubeSats](https://mlsanity.com/paper/2506.03152/) (2025)

## Abstract (arXiv)

Satellites have become more widely available due to the reduction in size and cost of their components. As a result, there has been an advent of smaller organizations having the ability to deploy satellites with a variety of data-intensive applications to run on them. One popular application is image analysis to detect, for example, land, ice, clouds, etc. for Earth observation. However, the resource-constrained nature of the devices deployed in satellites creates additional challenges for this resource-intensive application. In this paper, we present our work and lessons-learned on building an Image Processing Unit (IPU) for a satellite. We first investigate the performance of a variety of edge devices (comparing CPU, GPU, TPU, and VPU) for deep-learning-based image processing on satellites. Our goal is to identify devices that can achieve accurate results and are flexible when workload changes while satisfying the power and latency constraints of satellites. Our results demonstrate that hardware accelerators such as ASICs and GPUs are essential for meeting the latency requirements. However, state-of-the-art edge devices with GPUs may draw too much power for deployment on a satellite. Then, we use the findings gained from the performance analysis to guide the development of the IPU module for an upcoming satellite mission. We detail how to integrate such a module into an existing satellite architecture and the software necessary to support various missions utilizing this module.