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Roadmap on emerging hardware and technology for machine learning

机译：机器学习新兴硬件与技术的路线图

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Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.
机译：人工智能的最新进展很大程度上归功于机器学习的快速发展，尤其是在算法和神经网络模型方面。然而，正是硬件的性能，尤其是计算系统的能源效率，决定了机器学习能力的根本极限。以数据为中心的计算需要硬件系统的革命，因为基于晶体管和冯·诺依曼体系结构的传统数字计算机不是专门为神经形态计算设计的。基于新兴设备和新体系结构的硬件平台是未来计算的希望，可以显著提高吞吐量和能效。然而，构建这样一个系统面临着许多挑战，从材料选择、设备优化、电路制造和系统集成等等。本路线图的目的是展示可能有利于机器学习的新兴硬件技术的快照，为纳米技术读者提供这一新兴领域挑战和机遇的视角。

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