Agentic AI for Self-Adaptive Semiconductor Architectures in Intelligent Wireless Networks
Keywords:
Intelligent Wireless Networks, Agentic AI, Adaptive Semiconductor Architectures, Real-Time Network Adaptation, Reconfigurable and Programmable Hardware, Neuromorphic Processors, In-Memory and Near-Memory Computing, Ternary and Binary Computing, 3D Integration, Spectrum and Mobile Resource Management, Context-Aware Modulation and Coding, End-to-End Control Loops, AI-Driven Development Toolchains, Secure Hardware–Software Co-Design, Performance Metrics and Benchmarks, Evaluation Protocols, 6G Systems, Intelligent Network Control, Hardware–AI Co-Design, Autonomous Wireless Systems.Abstract
Intelligent wireless network systems must adapt in real time to channel conditions, mobile users, and traffic demands while complying with strict performance specifications. The presented study describes a foundation for agentic AI-driven adaptive semiconductor architectures, taking into account both technology and system-level considerations. With respect to technology, it focuses on recent semiconductor architectural trends inspired by early developments in AI computing. In particular, it examines reconfigurable and programmable hardware, neuromorphic processors, ternary and binary computing, in-memory and near-memory computing, and 3D integration. From a system perspective, it identifies mechanisms supporting real-time adaptation: spectrum and mobile resource management, context-aware modulation and coding schemes, and end-to-end control loops. In addition, it discusses AI-driven development toolchains, addressing tool automation, data privacy, and security issues. Finally, it proposes metrics, benchmarks, and evaluation protocols enabling a holistic assessment of adaptive semiconductor architectures for intelligent wireless networks. Attention to both technology and systems can steer forthcoming technology development towards the ultimate intelligent wireless systems and networks targeted by 6G and beyond.
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