A brain-inspired hardware architecture for evolutionary algorithms based on memristive arrays

Brain-inspired computing takes inspiration from the brain to create energy-efficient hardware systems for information processing, capable of performing highly sophisticated tasks. Systems built with emerging electronics, such as memristive devices, can achieve gains in speed and energy by mimicking the distributed topology of the brain. In this work, a brain-inspired hardware architecture for evolutionary algorithms is proposed based on memristive arrays, which can realize sparse and approximate computing as a result of the parallel analog computing characteristic of the memristive arrays. On this basis, an efficient evolvable brain-inspired hardware system is implemented. We experimentally show that the approach can offer at least a four orders of magnitude speed improvement. We also use experimentally grounded simulations to explore fault tolerance and different parameter settings in the implemented hardware system. The experimental results show that the evolvable hardware system, implemented based on the proposed hardware architecture, can continuously evolve toward a better system even if there are failures or parameter changes in the memristive arrays, demonstrating that the proposed hardware architecture has good adaptability and fault tolerance.