E2CDA: Type II: Self-Adaptive Reservoir Computing with Spiking Neurons: Learning Algorithms and Processor Architectures

虽然计算在许多学科中越来越以数据为中心，但在这个数据驱动的科学和工程时代，传统的计算机体系结构在满足不断升级的性能和能源效率需求方面的潜力有限。该项目旨在开发大脑启发的计算神经模型和自适应处理器架构，以便在广泛的应用中实现智能数据处理和学习。虽然是跨学科的，但这项工作将连接神经科学、人工神经网络、计算机体系结构和硬件工程。计划中的研究将为学生提供丰富的培训和教育机会，并产生新的课程。促进本科生和代表性不足的学生参与研究。该项目的成果将广泛传播。通过与半导体研究公司的互动，将积极寻求与美国工业界的研究合作。这项工作旨在通过模仿大脑如何表示、处理和从信息中学习，更具体地说，通过开发基于第三代峰值神经网络和高效自适应处理器架构的计算模型，获得类似大脑的学习性能。在所谓的水库计算框架内，所提出的神经模型模拟了大脑的关键特征，如基于脉冲时序的信息处理。此外，该项目将开发大脑启发的学习机制，以允许训练复杂的循环尖峰神经网络。将开发集成片上学习、轻量级运行时学习性能预测和能源管理的自适应处理器架构，以最大限度地提高系统能源效率，同时提供性能保证。

While computing has become increasingly data centric across many disciplines, conventional computer architectures have limited potential in meeting the escalating performance and energy efficiency needs in this era of data-driven science and engineering. This project aims to develop brain-inspired neural models of computation and adaptive processor architectures to enable intelligent data processing and learning in a wide range of applications. While being strongly interdisciplinary, this work will bridge neuroscience, artificial neural networks, computer architecture, and hardware engineering. The planned research will provide rich training and educational opportunities to students, and produce new curriculum. Research participation from undergraduate and underrepresented students will be promoted. The outcomes of this project will be broadly disseminated. Research collaboration with the US industry will be actively pursued via interaction with the Semiconductor Research Corporation. This work is aimed at attaining brain-like learning performance by imitating how the brain represents, processes, and learns from information, and more specifically, by developing models of computation based on the third-generation spiking neural networks, and efficient adaptive processor architectures. Within the framework of so called reservoir computing, the proposed neural models mimic key characteristics of the brain such as information processing based on spike timing. Furthermore, this project will develop brain-inspired learning mechanisms to allow training of complex recurrent spiking neural networks. Self-adaptive processor architectures with integrated on-chip learning, light-weight runtime learning performance prediction, and energy management will be developed to maximize system energy efficiency while providing a guarantee of performance.