Roadmap on emerging hardware and technology for machine learning

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.

Automated summary

Recent progress in artificial intelligence is primarily attributed to the rapid development of machine learning, but the performance and energy efficiency of hardware systems set fundamental limits on machine learning capabilities. Data-centric computing requires a revolution in hardware systems, with new hardware platforms offering hope for future computing with improved throughput and energy efficiency. However, challenges such as materials selection, device optimization, circuit fabrication, and system integration must be addressed in building such systems.