SHF: Small: Enabling New Machine-Learning Usage Scenarios with Software-Defined Hardware for Symbolic Regression
SHF:小型:通过用于符号回归的软件定义硬件启用新的机器学习使用场景
基本信息
- 批准号:1909244
- 负责人:
- 金额:$ 49.95万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-10-01 至 2024-09-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Despite the widespread success of machine learning, existing techniques have limitations and/or unattractive trade-offs that prohibit important usage scenarios, particularly in embedded and real-time systems. For example, artificial neural nets provide sufficient accuracy for many applications, but can be too computationally expensive for embedded usage and may require large training data sets that are impractical to collect for some applications. Even when executed with cloud computing, neural nets often require graphics-processing unit acceleration, which greatly increases power costs that can already dominate the total cost of ownership in large-scale data centers and supercomputers. Similarly, linear regression is a widely used machine-learning technique, but generally requires model specification or guidance by the user, which is prohibitive for difficult-to-understand phenomena and/or many-dimensional problems. This project shows that symbolic regression complements existing machine-learning techniques by providing attractive Pareto-optimal trade-offs that enable new machine-learning usage scenarios where existing technologies are prohibitive. These symbolic-regression benefits come from three key advantages: 1) automatic model discovery, 2) computational efficiency with minimal loss in capability compared to existing techniques, and 3) lower sensitivity to training set size. Despite being studied for decades, symbolic regression is generally limited to toy examples due to the challenge of searching an infinite solution space with numerous local optima. This project presents a solution that significantly advances the state-of-the-art via two primary contributions: 1) 1,000,000x acceleration of the symbolic-regression exploration process, and 2) fundamentally new exploration algorithms that are only possible with such significant acceleration. To accelerate the symbolic-regression exploration process, the investigators introduce software-defined hardware that re-configures every cycle to provide a solution-specific pipeline implemented as a virtual hardware overlay on field-programmable gate arrays. Although this acceleration by itself improves upon the state-of-the-art in symbolic regression considerably, the more important contribution is the enabling of new exploration algorithms that are not feasible without massive increases in performance. The investigators use this performance improvement to introduce a new hybrid exploration algorithm that performs multiple concurrent searches using different configurations of genetic programming and deterministic heuristics, combined with two new prediction mechanisms to avoid local optima: sub-tree look-ahead prediction and operator correlation.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.
尽管机器学习取得了广泛的成功,但现有技术存在限制和/或缺乏吸引力的权衡,这些限制和/或权衡阻碍了重要的使用场景,特别是在嵌入式和实时系统中。例如,人工神经网络为许多应用提供了足够的准确性,但对于嵌入式使用来说可能在计算上过于昂贵,并且可能需要大的训练数据集,对于某些应用来说,收集这些数据集是不切实际的。即使使用云计算执行,神经网络也经常需要图形处理单元加速,这大大增加了电力成本,而电力成本已经在大规模数据中心和超级计算机的总拥有成本中占据主导地位。类似地,线性回归是一种广泛使用的机器学习技术,但通常需要用户的模型规范或指导,这对于难以理解的现象和/或多维问题是禁止的。该项目表明,符号回归通过提供有吸引力的帕累托最优权衡来补充现有的机器学习技术,从而实现新的机器学习使用场景,其中现有技术是禁止的。这些符号回归的好处来自三个关键优势:1)自动模型发现,2)与现有技术相比,计算效率损失最小,3)对训练集大小的敏感性较低。尽管已经研究了几十年,但由于搜索具有许多局部最优解的无限解空间的挑战,符号回归通常仅限于玩具示例。该项目提出了一种解决方案,通过两个主要贡献显着推进了最先进的技术:1)符号回归探索过程的1,000,000倍加速,以及2)只有在这种显着加速下才可能实现的全新探索算法。为了加速符号回归探索过程,研究人员引入了软件定义的硬件,该硬件在每个周期都进行重新配置,以提供作为现场可编程门阵列上的虚拟硬件覆盖层实现的特定于解决方案的流水线。虽然这种加速本身大大提高了符号回归的最新技术水平,但更重要的贡献是启用了新的探索算法,这些算法在性能没有大幅提高的情况下是不可行的。研究人员利用这种性能改进引入了一种新的混合探索算法,该算法使用遗传编程和确定性算法的不同配置执行多个并发搜索,并结合两种新的预测机制以避免局部最优:子树外观-该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准。
项目成果
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Using FPGA Devices to Accelerate Tree-Based Genetic Programming: A Preliminary Exploration with Recent Technologies
使用FPGA器件加速基于树的遗传编程:最新技术的初步探索
- DOI:
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Crary, Christopher;Piard, Welsey;Stitt, Greg;Bean, Caleb;Hicks, Benjamin
- 通讯作者:Hicks, Benjamin
Work-in-Progress: Toward a Robust, Reconfigurable Hardware Accelerator for Tree-Based Genetic Programming
正在进行的工作:为基于树的遗传编程打造一个强大的、可重新配置的硬件加速器
- DOI:10.1109/cases55004.2022.00015
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Crary, Christopher;Piard, Wesley;Chesley, Britton;Stitt, Greg
- 通讯作者:Stitt, Greg
PANDORA: An Architecture-Independent Parallelizing Approximation-Discovery Framework
PANDORA:独立于架构的并行逼近发现框架
- DOI:10.1145/3391899
- 发表时间:2020
- 期刊:
- 影响因子:2
- 作者:Stitt, Greg;Campbell, David
- 通讯作者:Campbell, David
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Greg Stitt其他文献
Low-Latency, Line-Rate Variable-Length Field Parsing for 100+ Gb/s Ethernet
适用于 100 Gb/s 以太网的低延迟线速可变长度字段解析
- DOI:
10.1145/3626202.3637559 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Greg Stitt;Wesley Piard;Christopher Crary - 通讯作者:
Christopher Crary
Using FPGA devices to accelerate the evaluation phase of tree-based genetic programming: an extended analysis
- DOI:
10.1007/s10710-024-09505-2 - 发表时间:
2025-01-07 - 期刊:
- 影响因子:0.900
- 作者:
Christopher Crary;Wesley Piard;Greg Stitt;Benjamin Hicks;Caleb Bean;Bogdan Burlacu;Wolfgang Banzhaf - 通讯作者:
Wolfgang Banzhaf
Greg Stitt的其他文献
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{{ truncateString('Greg Stitt', 18)}}的其他基金
CAREER: Design Virtualization for Mainstream Programming of Reconfigurable Computers
职业:可重构计算机主流编程的设计虚拟化
- 批准号:
1149285 - 财政年份:2012
- 资助金额:
$ 49.95万 - 项目类别:
Continuing Grant
CSR: Small: Elastic Computing - An Enabling Technology for Transparent, Portable, and Adaptive Multi-Core Heterogeneous Computing
CSR:小型:弹性计算 - 透明、便携式和自适应多核异构计算的使能技术
- 批准号:
0914474 - 财政年份:2009
- 资助金额:
$ 49.95万 - 项目类别:
Standard Grant
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