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.

经典数字计算耗电、脆弱且难以与模拟现实世界连接。材料储层计算机（RC）等非常规计算机可以帮助克服这些问题，特别是能够执行可直接利用其材料成分的自然动力学的具体计算，从而大大降低功率需求。此类设备在原理上已得到验证，但系统需要扩大规模，以便为现实世界的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。我们的目标是开发组合多种材料 RC 的技术： 1. 使用类似的实例配置，提供可扩展性 2. 使用不同的实例配置，共同执行单个 RC 配置无法单独执行的任务。