Smart Semiconductors for Efficient 6G Hardware
Keywords:
AI-Assisted Semiconductor Architectures, Adaptive Hardware Architectures, Artificial Intelligence in Integrated Circuits, Machine Learning–Enabled Hardware, Deep Learning Accelerators, Energy-Efficient Circuit Design, Beyond Moore’s Law Paradigms, Hardware–Software Co-Design, On-Chip AI Controllers, Low-Power AI Accelerators, Adaptive Baseband Signal Processing, Real-Time Workload Adaptation, Communication Systems for 6G, Intelligent Radio Architectures, Dynamic Resource Allocation in Hardware, Area and Energy Optimization, AI-Driven Circuit Adaptation, Semiconductor Design Automation, Adaptive Signal and Channel Processing, Next-Generation Communication Hardware.Abstract
Humanity's foundational building blocks, science, technology, and communication, have changed the way we address challenging world problems. Over the past two decades, radio communication systems have been embedded more and more in a multitude of electronic devices, which has directly enabled an explosion of bandwidth requirements. Advanced integrated circuits have helped satisfy this demand, yet extension of semiconductor technology driven by design paradigms such as Moore's Law no longer provides the energy efficiency levels needed for an ever-growing volume of global data traffic. Research has started to address 6G technological targets, including more data and a fully connected world, but without the rapid data rate or world connectivity of previous generations. A new design paradigm based on Artificial Intelligence is transforming semiconductor devices by integrating specialized, low-power accelerators. These devices exploit Machine and Deep Learning to enhance performance, energy consumption, and area efficiency of the circuit but in a fixed, static way.
Enabling Artificial Intelligence-Assisted Semiconductor Adaptive Architectures is a sought-after goal since it can actively respond to variation in user demand conditions, and can directly map the baseband signal processing requirements of each data message over different time scales. These Adaptive Architectures can also fine-tune AI-enabled accelerators to cope with sudden changes in the signal or channel and are needed to integrate them into the circuit, since the Adaptive Architectures implement the hardware-software co-design methodology that responds dynamically to real-time variations in workload and operational constraints. Suitable hardware resources and auxiliary functions are also needed to integrate on-chip AI controllers reliably and efficiently.
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