UKRI-RCN: Exploiting the dynamics of self-timed machine learning hardware (ESTEEM)

UKRI-RCN：利用自定时机器学习硬件（ESTEEM）的动态

• 批准号: EP/X039943/1
• 负责人: Alexandre Yakovlev
• 金额: $ 106.61万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX039943%2F1
• 关键词: UKRI; RCN; Exploiting; dynamics; self

People can often see these days adverts on media company vans like "Grab your life by the Gigabits" (Virgin Media). Similar slogans appear on IT company flyers offering data analysis at Tera operations per second. They show the undeniable progress in technology, though still rarely we see performance growth per energy, for example Gigabits per Joule. And yet we are increasingly having to face with rising energy bills. As appetites for extending our intelligence wider and deeper into our everyday life steadily grow the grand challenge of ICT in making intelligence energy efficient becomes more and more evident. A significant role in this belongs to the research that aims at finding better methods for machine learning and data classification where both power and time for performing key operations in learning are reduced. In simple terms reducing power amounts to reducing average switching activity of electronic hardware, while reducing time means determining the moments when the learning actions have reached the state of sufficient quality. Self-time hardware, which works on the event-driven principles, in combination with novel machine learning methods, based on efficient approximation and Boolean logic as opposed to heavy arithmetic, gives this research a lever of innovation and potential impact against the state of the art.This project will investigate opportunities for improving performance and energy efficiency in artificial intelligence hardware created by the inherent time and power elasticity of self-timed circuits. The project will lay foundation to a new design methodology for building electronic devices and systems with machine learning (ML) capabilities at the micro- and nano-scale granularity. Those devices will be widely leveraged in many at-the-edge applications such as environmental sensors, traffic monitors, wearables, as well potential commodity ML-enhanced devices that can be used as building blocks in computer systems of the future. Micro- and nanoclassifiers and decision makers that can operate in real-time with power/energy efficiency are expected to find many 'light-weight' applications, so optimal (in terms of latency and energy) control is crucial. Here is an example of handwritten character recognition by an electronic pen with energy-harvested power. A reference class is given (e.g., digit "5"). Then, a few attempts in handwriting of digit 5 are made. During all these attempts training is performed. Then another reference class is given, and similar training is performed on it, and so on. The key requirements are to keep time spent limited and consumed energy minimised. Training is to be done to the best of the achievable accuracy. There are several trade-offs involved between speed and power and accuracy of learning. The success of the project will be measured in terms of the answers to the key research questions about the dynamics of machine learning in self-timed circuits; for example, whether the asynchronous design approach combined with the use of learning automata and logic-based inference will reach minimum energy point for a given machine learning problem. The project outcomes in theory and design methodology will be validated by means of extensive simulations, prototyping, IC fabrication and testing, and, ultimately, via an embodiment of the new hardware solutions into a concrete IoT application. A particularly challenging and breaking through validation will be the development and fabrication of the first asynchronous machine learning integrated circuit using flexible substrates.The practical impact of this research will be in the directions and methods of designing intelligent embedded electronics that will be capable of performing run-time classification of data obtained from environmental sensors, audio and image signals, as well as fast moving consumer goods (FMCG) and smart packaging using flexible IC technology.

如今，人们经常可以在媒体公司的货车上看到广告，比如“抓住你的生活”（维珍媒体）。类似的口号出现在IT公司的传单上，提供每秒Tera操作的数据分析。它们显示了不可否认的技术进步，尽管我们仍然很少看到单位能量的性能增长，例如单位焦耳的能量。然而，我们越来越多地不得不面对不断上涨的能源账单。随着将我们的智能更广泛和更深入地扩展到我们日常生活的欲望稳步增长，ICT在使智能节能方面的巨大挑战变得越来越明显。这方面的一个重要作用属于旨在寻找更好的机器学习和数据分类方法的研究，其中减少了在学习中执行关键操作的功率和时间。简而言之，降低功率相当于降低电子硬件的平均开关活动，而降低时间意味着确定学习动作已经达到足够质量的状态的时刻。基于事件驱动原理的自时间硬件，结合新颖的机器学习方法，基于有效的近似和布尔逻辑，而不是繁重的算术，为这项研究提供了一个创新的杠杆，并对最先进的技术产生了潜在的影响。该项目将调查由固有的时间和功率所创造的人工智能硬件的性能和能源效率的改善机会。自定时电路的弹性。该项目将为构建具有微米和纳米级粒度机器学习（ML）功能的电子设备和系统的新设计方法奠定基础。这些设备将广泛应用于许多边缘应用，例如环境传感器、交通监控器、可穿戴设备以及可用作未来计算机系统构建模块的潜在商品ML增强设备。能够以功率/能源效率实时操作的微纳米分类器和决策者有望找到许多“轻量级”应用，因此最佳（在延迟和能源方面）控制至关重要。这里是一个通过电子笔进行手写字符识别的例子，电子笔具有能量收集功率。给出了一个参考类（例如，数字“5”）。然后，进行几次手写数字5的尝试。在所有这些尝试中，进行训练。然后给出另一个参考类，并对其执行类似的训练，等等。关键要求是保持有限的时间和消耗的能量最小化。训练应尽可能达到最佳的准确性。在学习的速度、能力和准确性之间存在几个权衡。该项目的成功将通过回答关于自定时电路中机器学习动态的关键研究问题来衡量;例如，异步设计方法与使用学习自动机和基于逻辑的推理相结合是否会达到给定机器学习问题的最小能量点。理论和设计方法的项目成果将通过广泛的模拟，原型设计，IC制造和测试进行验证，并最终通过新硬件解决方案的实施例进入具体的物联网应用。一个特别具有挑战性和突破性的验证将是第一个使用柔性基板的异步机器学习集成电路的开发和制造。这项研究的实际影响将是设计智能嵌入式电子设备的方向和方法，这些电子设备将能够对从环境传感器、音频和图像信号获得的数据进行运行时分类，以及使用柔性IC技术的快速消费品（FMCG）和智能包装。