CAREER: Analog VLSI Integrated Circuits for Real-Time Neural Control

职业：用于实时神经控制的模拟 VLSI 集成电路

• 批准号: 0093915
• 负责人: Jennifer Hasler
• 金额: $ 37.5万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-06-15 至 2007-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0093915&HistoricalAwards=false
• 关键词: CAREER; Analog; VLSI; Integrated; Circuits

0093915HaslerThis project will develop a new class of chips capable of high-thruput learning on the chip itself, implementing new concepts of neural network learning which should allow the chips to learn how to control manufacturing plants or vehicles in a very generalized way, applicable to a wide class of tasks. As a testbed, the project will develop neural network learning algorithms and chips to improve the performance of advanced semiconductor fabrication designs being developed by the PI's collaborators at Georgia Tech. If successful, this work could significantly accelerate the use of learning systems for engineering applications in general. It could eventually allow a substantial improvement in effective computational throughput per chip, above and beyond the improvements possible through Moore's Law (the increase in the physical feature density and speed of chips), for a very broad range of information processing tasks.This project builds on the PI's prior work developing analog computable memories, or analog computing arrays, where instead of storing the analog values to be used by external processors, he uses the memoryelement itself to perform the computation. These systems are based on arrays of dense floating-gate transistors that provide nonvolatile storage, compute a product between stored weights and inputs, allow for programming that does not affect the computation, and adapt over time based on the information flowing through the chip. In principle, this technology permits the development of general purpose chips with throughput comparable to that of dedicated analog ASIC chips, but with a kind of universal flexibility due to the adaptation of the weights and the universal approximation capabilities of the underlying architectures.

0093915Hasler该项目将开发一种能够在芯片本身上进行高吞吐量学习的新型芯片，实施神经网络学习的新概念，这应该允许芯片学习如何以非常通用的方式控制制造工厂或车辆，适用于广泛的任务类别。 作为一个测试平台，该项目将开发神经网络学习算法和芯片，以提高 PI 的佐治亚理工学院合作者正在开发的先进半导体制造设计的性能。 如果成功，这项工作可以显着加速学习系统在一般工程应用中的使用。 它最终可以大幅提高每个芯片的有效计算吞吐量，超越摩尔定律（芯片物理特征密度和速度的增加）可能带来的改进，适用于非常广泛的信息处理任务。该项目构建PI 之前开发模拟可计算存储器或模拟计算阵列的工作，其中他没有存储供外部处理器使用的模拟值，而是使用存储器元件本身来执行计算。 这些系统基于密集的浮栅晶体管阵列，这些晶体管提供非易失性存储，计算存储的权重和输入之间的乘积，允许进行不影响计算的编程，并根据流经芯片的信息随时间进行调整。 原则上，该技术允许开发吞吐量与专用模拟 ASIC 芯片相当的通用芯片，但由于权重的调整和底层架构的通用逼近功能而具有通用的灵活性。