AI RESEARCH
Performance Analysis of Edge and In-Sensor AI Processors: A Comparative Review
arXiv CS.LG
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ArXi:2603.08725v1 Announce Type: cross 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.