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Indian Institute of Technology Delhi
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Controlled randomness in spiking neurons can significantly enhance edge hardware performance while minimizing area and noise sensitivity.
A unified suite of neuromorphic silicon blocks achieves efficient edge processing with low power consumption, paving the way for advanced probabilistic inference in compact hardware.
Escape the QML vendor lock-in: this framework-agnostic QNN lets you train once and deploy on any major quantum hardware platform with minimal overhead.
