An Automated Approach to Computer Hardware Design Using Genetic Algorithm Optimization

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Yingyun Wang, Wenyu Shi


The field of computer hardware design continues to evolve rapidly, driven by the ever-increasing demand for faster, more efficient, and specialized hardware solutions. Traditional methods of hardware design are often time-consuming, and labour-intensive, and may not fully exploit the vast design space available. In this context, genetic algorithms (GAs) have emerged as a promising approach for automating the design process, leveraging principles of natural selection and evolution to efficiently explore and optimize complex design spaces. This paper presents an automated approach to computer hardware design using genetic algorithm optimization. By harnessing the power of GAs, our method enables the exploration of diverse design possibilities and the generation of optimized hardware architectures tailored to specific requirements. We discuss the key components of our approach, including representation schemes for hardware designs, genetic operators for variation and selection, and fitness evaluation criteria. Furthermore, we highlight the advantages of employing GAs in hardware design, such as their ability to handle multi-objective optimization, adapt to changing design constraints, and efficiently search large solution spaces. To demonstrate the effectiveness of our approach, we present experimental results showcasing its applicability to various hardware design tasks, including the synthesis of digital circuits, the optimization of processor architectures, and the design of application-specific integrated circuits (ASICs). Through comparative analysis with traditional design methods, we illustrate the superior performance, flexibility, and scalability offered by our automated approach.

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