Abstract
Despite the promise of superior efficiency and scalability, real-world deployment of emerging nanoelectronic platforms for brain-inspired computing have been limited thus far, primarily because of inter-device variations and intrinsic non-idealities. In this work, mitigation of these issues is demonstrated by performing learning directly on practical devices through a hardware-in-loop approach, utilizing stochastic neurons based on heavy metal/ferromagnetic spin–orbit torque heterostructures. The probabilistic switching and device-to-device variability of the fabricated devices of various sizes is characterized to showcase the effect of device dimension on the neuronal dynamics and its consequent impact on network-level performance. The efficacy of the hardware-in-loop scheme is illustrated in a deep learning scenario achieving equivalent software performance. This work paves the way for future large-scale implementations of neuromorphic hardware and realization of truly autonomous edge-intelligent devices.
Original language | English (US) |
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Article number | 2300805 |
Journal | Advanced Intelligent Systems |
Volume | 6 |
Issue number | 7 |
DOIs | |
State | Published - Jul 2024 |
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Human-Computer Interaction
- Mechanical Engineering
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Materials Science (miscellaneous)