Investigating bio-inspired architectures for efficient computation

研究仿生架构以实现高效计算

• 批准号: 2894696
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2894696
• 关键词: Investigating; bio; inspired; architectures; efficient

Classical digital computing is power-hungry, fragile, and hard to interface to the analogue real world. Unconventional computers such as in materio Reservoir Computers (RCs) can help overcome these issues, particularly by being able to perform embodied computation that can directly exploit the natural dynamics of their material composition, thereby dramatically reducing the power requirements. Such devices have been proven in principle, but systems need to be scaled up to provide sufficient and appropriate computational power for real-world tasks. Furthermore, a range of device configurations is needed to support different computational tasks. Our aim is to develop techniques for combining multiple material RCs: 1. with similar instance configurations, to provide scalability 2. with diverse instance configurations, to perform together tasks that no single RC configuration can readily perform alone.

古典数字计算是渴望的，脆弱的，难以与模拟现实世界的接口。非常规计算机（例如在材料储层计算机（RC）中）可以帮助克服这些问题，尤其是通过执行可以直接利用其材料组成的自然动力学的体现计算，从而大大降低功率需求。此类设备原则上已被证明，但是需要扩展系统以为现实世界任务提供足够且适当的计算能力。此外，需要一系列设备配置来支持不同的计算任务。我们的目的是开发结合多种材料RCS的技术：1。具有相似的实例配置，提供可扩展性2。具有不同的实例配置，以执行没有单个RC配置可以轻松执行的任务。