CAREER: Programmable Smart Machines

职业：可编程智能机器

• 批准号: 1254029
• 负责人: Jonathan Appavoo
• 金额: $ 59.5万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1254029&HistoricalAwards=false
• 关键词: CAREER; Programmable; Smart; Machines

Faster computers have enabled advances in science, commerce and daily life. Unfortunately, computers have also become complex and more and more difficult to program efficiently. This trend threatens the sustainability of future advances. Perhaps, however, we can draw upon biologically inspired learning techniques to shed light into a new model of hybrid computers, a ``Programmable Smart Machine'', that inherently learns from its past behavior to automatically improve its performance without the burden of more complex programming. Specifically this work explores the addition of a smart memory to a computer that gives it the abilities to learn, store and exploit patterns in past execution to improve its performance.Central to this work is the introduction of a new kind of global long-term machine learning based 'cache' that can be viewed as an auto-associative memory. The 'cache' is fed raw low-level traces of execution, from which it extracts and stores commonly occurring patterns that can be recognized and predicted. The core execution process is modified to send the trace to the 'cache' and to exploit its feedback to enact acceleration. The long-term goal is a system whose performance improves with the size and contents of the 'cache', which can be constructed with local associative memory devices and a shared online repository that is contributed to and leveraged by many systems. In this way a kind of shared computational history is naturally created and exploited.This work experimentally explores questions with respect to concretizing the ``Programmable Smart Machine'' model. What are useful and tractable traces for detecting patterns in execution? Can current unsupervised deep learning techniques detect, store and recall useful patterns? How can the predictions from the machine learning based memory be utilized to automatically improve performance? How big does the machine learning based memory need to be to yield useful predictions and acceleration? This work explores these questions using simulation and controlled workload experiments to create complete traces including all instructions, register values, and I/O events. Using the traces, at least two deep learning approaches will be evaluated with respect to the number and size of patterns they recognize. The resulting trained models will be integrated into the published auto-parallelization methodology that established preliminary results for this work. The simulation infrastructure, trace data and experimental results will be made publicly available to enable broader study.This work produces unique trace data of computer operation. The PI has found that visual and audio presentations of the preliminary data reflect the kind of intuition that computer scientists develop about how computers work. This aspect will be leveraged to develop both a seminar, ``From Bits to Chess to Supercomputers'' and an associated``Computing Intuition'' website that engages K-12 students with computing.

更快的计算机使科学、商业和日常生活得以进步。 不幸的是，计算机也变得越来越复杂，越来越难以有效地编程。 这一趋势威胁到未来进步的可持续性。 然而，也许我们可以利用生物启发的学习技术来揭示一种新的混合计算机模型，一种“可编程智能机器”，它可以从过去的行为中学习，从而自动提高其性能，而无需更复杂的编程负担。 具体来说，这项工作探讨了一个智能内存添加到计算机，使其能够学习，存储和利用过去的执行模式，以提高其性能。这项工作的核心是引入一种新的全球长期机器学习为基础的“缓存”，可以被视为一个自动联想记忆。 “缓存”接收原始的低级执行痕迹，从中提取并存储可以识别和预测的常见模式。 核心执行过程被修改为将跟踪发送到“缓存”，并利用其反馈来执行加速。 长期目标是一个系统，其性能随着“缓存”的大小和内容而提高，缓存可以用本地关联存储器设备和共享的在线存储库来构建，该存储库为许多系统提供并利用。 通过这种方式，一种共享的计算历史是自然地创建和exploited.This工作的实验探索的问题，具体化的“可编程智能机”模型。 对于检测执行模式来说，有哪些有用且易于处理的跟踪？ 当前的无监督深度学习技术能否检测、存储和召回有用的模式？ 如何利用基于机器学习的记忆的预测来自动提高性能？ 基于机器学习的内存需要多大才能产生有用的预测和加速？ 这项工作探讨了这些问题，使用模拟和受控的工作负载实验，以创建完整的跟踪，包括所有的指令，寄存器值，和I/O事件。 使用这些痕迹，至少有两种深度学习方法将根据它们识别的模式的数量和大小进行评估。 由此产生的训练模型将被集成到已发布的自动并行化方法中，该方法为这项工作建立了初步结果。 模拟基础设施、跟踪数据和实验结果将公开，以便进行更广泛的研究。这项工作产生了计算机操作的独特跟踪数据。 PI发现，初步数据的视觉和听觉呈现反映了计算机科学家对计算机如何工作的直觉。 这方面将被利用来开发一个研讨会，“从比特到国际象棋到超级计算机”和一个相关的"计算直觉“网站，让K-12学生参与计算。