Performance Analysis of Edge and In-Sensor AI Processors: A Comparative Review
arXiv cs.LG / 3/11/2026
Signals & Early TrendsIdeas & Deep Analysis
Key Points
- The article reviews the current landscape of ultra-low-power edge AI processors, including heterogeneous SoCs, neural accelerators, and in-sensor compute architectures, focusing on their design and suitability for always-on, latency-sensitive AI workloads.
- It categorizes commercially available and research-grade platforms by compute paradigms, power consumption, and memory hierarchies, providing a comprehensive architectural overview.
- Empirical benchmarking was conducted using a 336M MAC segmentation model (PicoSAM2) on three representative processors: GAP9 (multi-core RISC-V with accelerators), STM32N6 (ARM Cortex-M55 with neural accelerator), and Sony IMX500 (in-sensor CMOS compute).
- Results showed significant variation in performance metrics, with Sony IMX500 leading in utilization and energy-delay product efficiency, GAP9 excelling in energy efficiency within microcontroller-class budgets, and STM32N6 providing the lowest latency but with higher energy costs.
- The review highlights emerging design trends and the practical trade-offs informing the evolution of ultra-low-power and in-sensor AI processing technologies, underscoring the growing maturity of in-sensor AI hardware.
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