CRI: CI-NEW: Trainable Reconfigurable Development Platform for Large-Scale Neuromorphic Cognitive Computing

CRI：CI-NEW：用于大规模神经形态认知计算的可训练可重构开发平台

• 批准号: 1823366
• 负责人: Gert Cauwenberghs
• 金额: $ 150万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1823366&HistoricalAwards=false
• 关键词: CRI; CI; NEW; Trainable; Reconfigurable

Neuromorphic cognitive computing aims at learning to solve complex cognitive tasks by emulating the principles and physical organization of highly efficient and resilient adaptive information processing in the biological brain. Despite over 30 years of development and a recent surge of broad interest across all Science, Technology, Engineering and Mathematics (STEM) disciplines, access to neuromorphic cognitive computing remains mostly limited to a small community of highly trained researchers in the field due to high entry barriers and costs associated with the specialized nature and complex operation of currently available systems. This project will construct and support a general-purpose neuromorphic cognitive computing platform that will be the largest and most versatile realized to date as well as the first to be broadly available and open to the research community at large, for research into new forms of brain-inspired computing that are more effective and more efficient in approaching the cognitive capabilities of the human mind. Targeting wide adoption by a diverse cross-section of users in the broader STEM research community, the platform will feature a natural user interface that shields novice users from the challenges arising in operating and configuring highly specialized neuromorphic hardware, by providing a set of user-friendly software tools maintained by and shared with the user community. Building on extensive existing network and storage infrastructure for user access and data sharing at the San Diego Supercomputer Center, the platform will be hosted and maintained through the Neuroscience Gateway (NSG) Portal, which currently serves over 600 active users in the scientific community.The large-scale neuromorphic platform will serve as a new and unparalleled resource to the Computer and Information Science and Engineering (CISE) research community, addressing a great need for an experimental testbed for research in alternative forms of computing beyond the traditional von Neumann paradigm and the impending physical limits to Moore's Law expansion in the scaling of computing technology. The reconfigurable platform will feature a hierarchically interconnected network of in-memory computing processing nodes that emulates, in real-time, highly flexible neural dynamics (integrate-and-fire, graded, stochastic binary, etc) of up to 128 million neurons with high flexible connectivity and plasticity (spike-timing dependent plasticity, gradient-based deep learning, etc) of up to 32 billion synapses. The system will be capable of biophysical detail in computational neuroscience modeling, as well as high performance and efficiency in on-line adaptive pattern recognition, serving and bringing together both computational neuroscience and computational intelligence communities that have traditionally pursued disparate computational approaches. The user interface of the platform will support software tools and resources for deep learning and run-time optimization in artificial intelligence applications, and for interference of structure and functional connectivity from recorded neural activity in computational neuroscience research, among others. To facilitate greatest scientific and societal impact, the infrastructure will be made available free of charge, on a time-managed shared basis, to any researcher in return for agreeing to share source code and data necessary to replicate results reported in the literature.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

神经形态认知计算旨在通过模拟生物大脑中高效和弹性自适应信息处理的原理和物理组织来学习解决复杂的认知任务。 尽管经过30多年的发展，最近所有科学、技术、工程和数学（STEM）学科都引起了广泛的兴趣，但由于进入门槛高以及与当前可用系统的专业性质和复杂操作相关的成本，神经形态认知计算的使用仍然主要限于该领域训练有素的研究人员组成的小社区。 该项目将构建和支持一个通用的神经形态认知计算平台，该平台将是迄今为止实现的最大和最通用的平台，也是第一个广泛提供并向研究界开放的平台，用于研究新形式的大脑启发计算，这些计算在接近人类思维的认知能力方面更有效，更高效。 该平台的目标是在更广泛的STEM研究社区中广泛采用不同的用户，该平台将提供一个自然的用户界面，通过提供一套用户友好的软件工具，保护新手用户免受操作和配置高度专业化的神经形态硬件所带来的挑战。 该平台建立在圣地亚哥超级计算机中心用于用户访问和数据共享的广泛的现有网络和存储基础设施基础上，将通过神经科学网关（NSG）门户网站进行托管和维护，目前为科学界的600多名活跃用户提供服务。大规模神经形态平台将成为计算机和信息科学与工程（CISE）的一个新的和无与伦比的资源研究社区，解决了一个实验测试平台的研究，在替代形式的计算超越了传统的冯诺依曼范式和即将到来的物理限制，摩尔定律的扩展计算技术的缩放的巨大需求。 可重新配置的平台将具有内存计算处理节点的分层互连网络，该网络实时仿真高达1.28亿个神经元的高度灵活的神经动力学（集成和发射，分级，随机二进制等），具有高达320亿个突触的高度灵活的连接性和可塑性（尖峰定时依赖可塑性，基于梯度的深度学习等）。 该系统将能够在计算神经科学建模的生物物理细节，以及在线自适应模式识别的高性能和效率，服务和汇集计算神经科学和计算智能社区，传统上追求不同的计算方法。 该平台的用户界面将支持软件工具和资源，用于人工智能应用中的深度学习和运行时优化，以及计算神经科学研究中记录的神经活动的结构和功能连接的干扰等。 为了促进最大的科学和社会影响，该基础设施将免费提供，在时间管理共享的基础上，任何研究人员同意共享源代码和数据，以复制文献中报告的结果。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

A 1.52 pJ/Spike Reconfigurable Multimodal Integrate-and-Fire Neuron Array Transceiver

1.52 pJ/Spike 可重构多模态集成发射神经元阵列收发器

• DOI: 10.1145/3407197.3407209
• 发表时间: 2020
• 期刊: 2020 ACM Int. Conf. on Neuromorphic Systems (ICONS’2020
• 作者: Kubendran, Rajkumar;Wan, Weier;Joshi, Siddharth;Wong, H.-S. Philip;Cauwenberghs, Gert
• 通讯作者: Cauwenberghs, Gert

Neuromorphic Dynamical Synapses With Reconfigurable Voltage-Gated Kinetics

• DOI: 10.1109/tbme.2019.2948809
• 发表时间: 2020-07
• 期刊: IEEE Transactions on Biomedical Engineering
• 影响因子: 4.6
• 作者: Jun Wang;G. Cauwenberghs;F. Broccard

Brain-Inspired Learning on Neuromorphic Substrates

• DOI: 10.1109/jproc.2020.3045625
• 发表时间: 2020-10
• 期刊: Proceedings of the IEEE
• 影响因子: 20.6
• 作者: Friedemann Zenke;E. Neftci

Dropout and DropConnect for Reliable Neuromorphic Inference Under Communication Constraints in Network Connectivity

Dropout 和 DropConnect 在网络连接通信约束下实现可靠​​的神经形态推理

• DOI: 10.1109/jetcas.2019.2952642
• 发表时间: 2019
• 期刊: IEEE Journal on Emerging and Selected Topics in Circuits and Systems
• 影响因子: 4.6
• 作者: Sakai, Yasufumi;Pedroni, Bruno U.;Joshi, Siddharth;Tanabe, Satoshi;Akinin, Abraham;Cauwenberghs, Gert
• 通讯作者: Cauwenberghs, Gert

Utilizing Deep Learning Towards Multi-Modal Bio-Sensing and Vision-Based Affective Computing

• DOI: 10.1109/taffc.2019.2916015
• 发表时间: 2022-01-01
• 期刊: IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
• 影响因子: 11.2
• 作者: Siddharth;Jung, Tzyy-Ping;Sejnowski, Terrence J.
• 通讯作者: Sejnowski, Terrence J.