# Performance Analysis of Edge and In-Sensor AI Processors: A Comparative Review

arXiv: https://arxiv.org/abs/2603.08725 · HTML: https://mlsanity.com/paper/2603.08725/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 60 references that are in the map.
- First posted on arXiv 18 Feb 2026.

## Builds on (most cited first, 22 of 22)

- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [Binary Neural Networks: A Survey](https://mlsanity.com/paper/2004.03333/) (2020)
- [Efficient Neuromorphic Signal Processing with Loihi 2](https://mlsanity.com/paper/2111.03746/) (2021)
- [Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models](https://mlsanity.com/paper/2503.06027/) (2025)
- [Efficient Video and Audio processing with Loihi 2](https://mlsanity.com/paper/2310.03251/) (2023)
- [A Machine Learning-oriented Survey on Tiny Machine Learning](https://mlsanity.com/paper/2309.11932/) (2023)
- [Retina : Low-Power Eye Tracking with Event Camera and Spiking Hardware](https://mlsanity.com/paper/2312.00425/) (2023)
- [Green Edge AI: A Contemporary Survey](https://mlsanity.com/paper/2312.00333/) (2023)
- [A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms](https://mlsanity.com/paper/2306.15552/) (2023)
- [TinyTracker: Ultra-Fast and Ultra-Low-Power Edge Vision In-Sensor for Gaze Estimation](https://mlsanity.com/paper/2307.07813/) (2023)
- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [GAP9Shield: A 150GOPS AI-capable Ultra-low Power Module for Vision and Ranging Applications on Nano-drones](https://mlsanity.com/paper/2407.13706/) (2024)
- [Benchmarking Ultra-Low-Power μNPUs](https://mlsanity.com/paper/2503.22567/) (2025)
- [PicoSAM2: Low-Latency Segmentation In-Sensor for Edge Vision Applications](https://mlsanity.com/paper/2506.18807/) (2025)
- [An Efficient Ground-aerial Transportation System for Pest Control Enabled by AI-based Autonomous Nano-UAVs](https://mlsanity.com/paper/2502.14455/) (2025)
- [In-Ear-Voice: Towards Milli-Watt Audio Enhancement With Bone-Conduction Microphones for In-Ear Sensing Platforms](https://mlsanity.com/paper/2309.02393/) (2023)
- [End-to-End Efficiency in Keyword Spotting: A System-Level Approach for Embedded Microcontrollers](https://mlsanity.com/paper/2509.07051/) (2025)
- [TinBiNN: Tiny Binarized Neural Network Overlay in about 5,000 4-LUTs and 5mW](https://mlsanity.com/paper/1903.06630/) (2019)
- [Sub-Millisecond Event-Based Eye Tracking on a Resource-Constrained Microcontroller](https://mlsanity.com/paper/2508.13244/) (2025)
- [A Distributed Emulation Environment for In-Memory Computing Systems](https://mlsanity.com/paper/2510.08257/) (2025)
- [Lincoln AI Computing Survey (LAICS) and Trends](https://mlsanity.com/paper/2510.20931/) (2025)

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

- [TinyML Enhances CubeSat Mission Capabilities](https://mlsanity.com/paper/2603.20174/) (2026)
- [TinyGLASS: Real-Time Self-Supervised In-Sensor Anomaly Detection](https://mlsanity.com/paper/2603.16451/) (2026)
- [Exploiting In-Sensor Computing for Energy-Efficient Earth Observation](https://mlsanity.com/paper/2606.01271/) (2026)
- [Event-Based Vision in Space: Applications, Trends, and Future Directions](https://mlsanity.com/paper/2606.01280/) (2026)

## Abstract (arXiv)

This review examines the rapidly evolving landscape of ultra-low-power edge processors, covering heterogeneous Systems-on-Chips (SoCs), neural accelerators, near-sensor and in-sensor architectures, and emerging dataflow and memory-centric designs. We categorize commercially available and research-grade platforms according to their compute paradigms, power envelopes, and memory hierarchies, and analyze their suitability for always-on and latency-critical Artificial Intelligence (AI) workloads. To complement the architectural overview with empirical evidence, we benchmark a 336 million Multiply-Accumulate (MAC) segmentation model (PicoSAM2) on three representative processors: GAP9, leveraging a multi-core RISC-V architecture augmented with hardware accelerators; the STM32N6, which pairs an advanced ARM Cortex-M55 core with a dedicated neural architecture accelerator; and the Sony IMX500, representing in-sensor stacked-Complementary Metal-Oxide-Semiconductor (CMOS) compute. Collectively, these platforms span MCU-class, embedded neural accelerator, and in-sensor paradigms. The evaluation reports latency, inference efficiency, energy efficiency, and energy-delay product. The results show a clear divergence in hardware behavior, with the IMX500 achieving the highest utilization (86.2 MAC/cycle) and the lowest energy-delay product, highlighting the growing significance and technological maturity of in-sensor processing. GAP9 offers the best energy efficiency within microcontroller-class power budgets, and the STM32N6 provides the lowest raw latency at a significantly higher energy cost. Together, the review and benchmarks provide a unified view of the current design directions and practical trade-offs that are shaping the next generation of ultra-low-power and in-sensor AI processors.