Navnidhi K. Upadhyay; Hao Jiang; Zhongrui Wang; Shiva Asapu; Qiangfei Xia; J. Joshua Yang · 2019 · Advanced Materials Technologies
Paper
Abstract A neuromorphic computing system may be able to learn and perform a task on its own by interacting with its surroundings. Combining such a chip with complementary metal–oxide–semiconductor (CMOS)‐based processors can potentially solve a variety of problems being faced by today's artificial intelligence (AI) systems. Although various architectures purely based on CMOS are designed to maximize the computing efficiency of AI‐based applications, the most fundamental operations including matrix multiplication and convolution heavily rely on the CMOS‐based multiply–accumulate units which are ultimately limited by the von Neumann bottleneck. Fortunately, many emerging memory devices can naturally perform vector matrix multiplication directly utilizing Ohm's law and Kirchhoff's law when an array of such devices is employed in a cross‐bar architecture. With certain dynamics, these devices can also be used either as synapses or neurons in a neuromorphic computing system. This paper discusses various emerging nanoscale electronic devices that can potentially reshape the computing paradigm in the near future.
Analysis
This paper reviews emerging nanoscale electronic devices, particularly memristors, that can overcome the von Neumann bottleneck and enable efficient neuromorphic computing by performing vector-matrix multiplication and emulating synaptic/neuronal functions.
Discovery
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