An Electromagnetic Perspective of Artificial Intelligence Neuromorphic Chips

Abstract: The emergence of artificial intelligence has shown a huge promise in solving a wide range of complex problems. However, traditional general-purpose chips based on von Neumann architecture face the ”memory wall” problem when applied in artificial intelligence applications. Based on the efficiency of the human brain, many intelligent neuromorphic chips have been proposed to emulate its working mechanism and neuron-synapse structure. With the emergence of spiking-based neuromorphic chips, the computation and energy efficiency could be enhanced by integrating a variety of features inspired by the biological brain. Aligning with the rapid development of neuromorphic chips, it is of great importance to investigate the electromagnetic interference and signal integrity issues in neuromorphic chips for both CMOS-based and memristor-based circuits. Here, we review neuromorphic circuits design, algorithms and applications. With focus on signal integrity issues, modeling, and optimization. Moreover, the heterogeneous structures of neuromorphic circuits and other circuits such as memory arrays, sensors using different integration technologies is also reviewed, and locations where signal integrity might be compromised is exposed. Finally, we provide signal integrity future trends and prospects in neuromorphic devices

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