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XPS: FULL: Emerging Nonvolatile Memory for Analog-iterative Numerical Methods

XPS：FULL：用于模拟迭代数值方法的新兴非易失性存储器

基本信息

批准号：
1628384
负责人：
Mikko Lipasti
金额：
$ 82.5万
依托单位：
University of Wisconsin-Madison
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1628384&HistoricalAwards=false
关键词：
XPS FULL Emerging Nonvolatile Memory

项目摘要

A new type of computer memory - crosspoint resistive memory - hasemerged as a likely candidate to replace current memory technology infuture computing systems. This memory allows for potential computerdesigns with high memory capacity and with memory incorporateddirectly into processing units. Novel thinking about computationalmethods is required to exploit the potential of these novelsystems. This project will explore new, fundamental methods in thefield of numerical optimization that are suited to implementation oncomputer systems that incorporate crosspoint resistive memory. Thefield of optimization is chosen as a testbed because of its importanceto a wide range of scientific disciplines.Crosspoint memory has unprecedented advantages in capacity and accesslatency. Substantial innovation is required to fully exploit thepotential benefits of integrating memory into processing units;current algorithms are unsuitable, because they are constrained by thevon Neumann bottleneck. The PIs will design the Gigascale Analog IterativeNetwork Solver (GAINS), a system architecture to enable efficientin-situ data processing. GAINS alters the application-, architecture-,and logic/circuit-level abstractions that enable designers anddevelopers at each layer to work independently. (i) It promotesmatrices to a first-class data type. (ii) It integrates computataionand memory, avoiding pitfalls of conventional memoryhierarchies. (iii) It exploits multi-valued representations in storageand computation. (iv) It replaces binary logic circuits withmulti-valued analog circuits, reducing area overhead and powerconsumption. The PIs will investigate the effects of this changed paradigmon the design of algorithms in numerical optimization and machinelearning.

一种新型的计算机存储器--交叉点电阻存储器--已经成为未来计算系统中取代当前存储器技术的可能候选者。这种存储器允许潜在的计算机设计与高存储容量和存储器直接扩展到处理单元。需要对计算方法进行新的思考，以利用这些新系统的潜力。这个项目将探索新的，在数值优化领域的基本方法，适用于实施对计算机系统，包括交叉点电阻存储器。交叉点存储器在容量和访问延迟方面具有前所未有的优势，因此，交叉点存储器被选为优化领域的试验平台，因为它对广泛的科学学科具有重要意义。要充分利用将存储器集成到处理单元中的潜在好处，需要大量的创新;目前的算法是不合适的，因为它们受到冯·诺依曼瓶颈的限制。PI将设计千兆级模拟迭代网络解算器（GAINS），这是一个能够有效进行现场数据处理的系统架构。GAINS改变了应用程序、体系结构和逻辑/电路级的抽象，使每一层的设计人员和开发人员能够独立工作。(i)它将矩阵提升为一级数据类型。(ii)它集成了计算和内存，避免了传统内存层次结构的缺陷。(iii)它利用了存储和计算中的多值表示。 (iv)它用多值模拟电路代替了二进制逻辑电路，减少了面积开销和功耗。PI将研究这种改变的范例对数值优化和机器学习算法设计的影响。