To inaugurate energy-efficient hardware as a solution to complex tasks, information processing paradigms shift from von Neumann to non-von Neumann computing architectures. Emerging electronic devices compete with speed, energy, and performance to revolutionize the neural hardware system where training and inference must achieve milestones. In this Perspective, we discuss the essential criteria for training and inference in various nonvolatile neuromorphic systems such as filamentary resistive switching, interfacial resistive switching, electrochemical random-access memory, and ferroelectric memory. We present a holistic analysis of technical requirements to design ideal neuromorphic hardware in which linearity is the critical aspect during training, whereas retention is the essential criterion of inference. Finally, we evaluate the prospect of a futuristic neuromorphic hardware system by optimizing the training and inference dilemma.
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7 February 2022
Perspective|
February 07 2022
Prospect and challenges of analog switching for neuromorphic hardware
Special Collection:
Neuromorphic Computing: From Quantum Materials to Emergent Connectivity
Writam Banerjee
;
Writam Banerjee
a)
Center for Single Atom-based Semiconductor Device, Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH)
, Pohang 37673, Republic of Korea
a)Authors to whom correspondence should be addressed: writam.banerjee@gmail.com and hwanghs@postech.ac.kr
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Revannath Dnyandeo Nikam;
Revannath Dnyandeo Nikam
Center for Single Atom-based Semiconductor Device, Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH)
, Pohang 37673, Republic of Korea
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Hyunsang Hwang
Hyunsang Hwang
a)
Center for Single Atom-based Semiconductor Device, Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH)
, Pohang 37673, Republic of Korea
a)Authors to whom correspondence should be addressed: writam.banerjee@gmail.com and hwanghs@postech.ac.kr
Search for other works by this author on:
a)Authors to whom correspondence should be addressed: writam.banerjee@gmail.com and hwanghs@postech.ac.kr
Appl. Phys. Lett. 120, 060501 (2022)
Article history
Received:
September 30 2021
Accepted:
January 22 2022
Citation
Writam Banerjee, Revannath Dnyandeo Nikam, Hyunsang Hwang; Prospect and challenges of analog switching for neuromorphic hardware. Appl. Phys. Lett. 7 February 2022; 120 (6): 060501. https://doi.org/10.1063/5.0073528
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