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An AI-Assisted Approach to Arithmetic Logic Unit (ALU) Design Optimization for Enhanced Processor Performance and Efficiency

Oct 2026 · Zenodo (CERN European Organization for Nuclear Research)
Low-power high-performance VLSI design

Abstract

This research explores how Artificial Intelligence can enhance the optimization of Arithmetic Logic Units (ALUs), which are crucial for processor speed, energy efficiency, and silicon area. Traditional ALU design relies on fixed architectures and rule-based heuristics, but the proposed framework integrates Graph Neural Networks for early performance prediction, Reinforcement Learning for logic restructuring, and Large Language Models for initial RTL generation, all under a strict verification gate to ensure correctness. Compared to conventional methods, this AI-assisted approach enables broader design-space exploration, hybrid adder structures, faster trade-off evaluation, and potential transfer learning across technology libraries. While promising for improving processor efficiency and supporting error-tolerant workloads, challenges remain in data availability, correctness assurance, computational resource needs, and the risk of over-reliance on AI tools.

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