A Review of Embedded Artificial Intelligence Research (2023–2026): Technological Advancements, Representative Advances, and Future Prospects
Zhaoyun Zhang · Micromachines · 2026
Since the publication of the "Review of Embedded Artificial Intelligence Research" in 2023, driven by innovations in hardware architectures, advances in lightweight algorithms, and the maturation of edge-cloud collaboration technologies, embedded artificial intelligence (embedded AI) has progressed from "technically feasible" to "large-scale deployment". As a continuation of that review, this article systematically surveys the core advances in embedded AI from 2023 to 2026. At the hardware level, it examines engineering progress in non-von Neumann architectures such as compute-in-memory and neuromorphic chips, as well as heterogeneous integration technologies. At the algorithmic level, it covers dynamic adaptive lightweighting, specialized edge-side optimization of large models (including on-device large language model fine-tuning and edge diffusion models), and lightweight multimodal approaches. In terms of deployment paradigms, it discusses edge-side full training, federated edge learning, edge-cloud collaborative intelligence, and emerging paradigms. At the application level, it illustrates the "perception-decision-execution" pipeline in industrial IoT, wearable healthcare, autonomous driving, embodied intelligence, and smart agriculture. The article also analyzes core challenges including ultra-low-power design for extreme scenarios, cross-platform standardization, edge-side data security and privacy, and model robustness in complex environments. Based on these findings, four research directions are proposed to guide future work.