SHF Small: Hierarchical Unsupervised Inference Using Robust Neuromorphic Computation

SHF Small：使用鲁棒神经形态计算的分层无监督推理

• 批准号: 1218492
• 负责人: Jeremiah Holleman
• 金额: $ 25万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218492&HistoricalAwards=false
• 关键词: SHF; Small; Hierarchical; Unsupervised; Inference

The human brain contains roughly 100 billion neurons. Each operates at approximately 150 Hz, far slower than modern digital processors, suggesting that the brain's computational strength stems from its massively parallel architecture rather than sheer processing speed. Deep machine learning (DML) has recently emerged as a promising framework for mimicking the information representation capabilities of the brain. Inspired by discoveries in neurobiology, hidden layers of deep learning systems encode hierarchically distributed representations of complex inputs. However, the fundamental mismatch between a highly parallel architecture and the serial structure of conventional processors limits the scalability of software-based DML systems. By fully leveraging the computational power of individual transistors, analog neuromorphic circuits achieve much greater density and energy efficiency than digital technology. The goal of this research is to use neuromorphic analog computational elements to enable scalable deep learning systems.The proposed research offers the potential to revolutionize the design and utilization of large-scale deep machine learning systems with applications to real-world, complex, high-dimensional pattern recognition problems. In particular, the study of floating-gate circuitry for realizing such systems is expected to have broad impact on the field of neuromorphic engineering. The long-range impact of compact, power-efficient learning systems will be profound, with benefits to society ranging from micro-scale health-monitoring sensors capable of learning from their own observations to large-scale autonomous processing of multimedia data. The interdisciplinary nature of the project will provide an opportunity for graduate and undergraduate students to work at the intersection of different specializations and provide a unique platform on which to study the emergent properties of complex biologically-inspired systems.

人类大脑包含大约1000亿个神经元。每一个都以大约150 Hz的频率运行，比现代数字处理器慢得多，这表明大脑的计算能力源于其大规模并行架构，而不是纯粹的处理速度。深度机器学习（DML）最近已经成为一个很有前途的框架，用于模仿大脑的信息表示能力。受神经生物学发现的启发，深度学习系统的隐藏层对复杂输入的分层分布式表示进行编码。然而，高度并行架构和传统处理器的串行结构之间的根本不匹配限制了基于软件的DML系统的可扩展性。通过充分利用单个晶体管的计算能力，模拟神经形态电路实现了比数字技术更高的密度和能效。这项研究的目标是使用神经形态模拟计算元素来实现可扩展的深度学习系统。拟议的研究提供了彻底改变大规模深度机器学习系统的设计和利用的潜力，并将其应用于现实世界，复杂，高维模式识别问题。特别是，浮栅电路实现这样的系统的研究，预计将有广泛的影响神经形态工程领域。紧凑、节能的学习系统的长期影响将是深远的，从能够从自己的观察中学习的微型健康监测传感器到大规模自主处理多媒体数据，都将给社会带来好处。该项目的跨学科性质将为研究生和本科生提供一个在不同专业交叉点工作的机会，并提供一个独特的平台来研究复杂生物启发系统的新兴特性。