桥接带隙：设备物理学家需要了解的机器学习
Bridging the Band Gap: What Device Physicists Need to Know About Machine Learning
摘要：
本文概述了半导体材料和器件研究的前景，以加速机器学习（ML）算法。我们观察到半导体和器件物理和工程社区与数字逻辑和计算机硬件架构社区之间的脱节。本文首先概述了计算复杂性的原理和计算的基本物理限制及其与物理系统的关系。然后，本文通过介绍ML系统的三个关键组件来介绍ML：表示，评估和优化。然后，文章讨论并提供了从半导体和设备物理领域的新兴技术的应用程序的例子，作为解决计算问题，以及计算应用的新兴设备的简要概述。然后，本文回顾了ML加速器的前景，将固定功能和可重编程数字逻辑与忆阻器、电阻存储器、磁存储器和概率位等新型器件进行了比较。与基于数字互补金属氧化物半导体（CMOS）技术的ML加速器相比，我们观察到基于新型器件和材料的ML加速器的性能普遍较低，特别是在MNIST光学字符识别任务（一种常见的ML基准）中，并且还强调了基于新型材料和器件的方法缺乏进展趋势。最后，本文提出了有意义的评估和比较不同的ML实现的优点，希望通过为他们的工作提供一个共同的参考框架，促进材料科学，器件物理，数字逻辑和计算机体系结构社区之间的对话。
英文摘要：
This article surveys the landscape of semiconductor materials and devices research for the acceleration of machine learning (ML) algorithms. We observe a disconnect between the semiconductor and device physics and engineering communities, and the digital logic and computer hardware architecture communities. The article first provides an overview of the principles of computational complexity and fundamental physical limits to computing and their relation to physical systems. The article then provides an introduction to ML by presenting three key components of ML systems: representation, evaluation, and optimisation. The article then discusses and provides examples of the application of emerging technologies from the demiconductor and device physics domains as solutions to computational problems, alongside a brief overview of emerging devices for computing applications. The article then reviews the landscape of ML accelerators, comparing fixed-function and reprogrammable digital logic with novel devices such as memristors, resistive memories, magnetic memories, and probabilistic bits. We observe broadly lower performance of ML accelerators based on novel devices and materials when compared to those based on digital complimentary metal-oxide semiconductor (CMOS) technology, particularly in the MNIST optical character recognition task, a common ML benchmark, and also highlight the lack of a trend of progress in approaches based on novel materials and devices. Lastly, the article proposes figures of merit for meaningful evaluation and comparison of different ML implementations in the hope of fostering a dialogue between the materials science, device physics, digital logic, and computer architecture communities by providing a common frame of reference for their work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
