Analog-aware block Jacobi schemes in flexible GMRES maintain convergence under simulated device non-idealities when block size, damping, and approximation accuracy are chosen to account for analog scaling, noise, quantization, and clipping.
a ndli, A. Singh, S. M. M \
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Neuromorphic computing using compute-in-memory, analog dynamics, and sparse brain-inspired communication offers a route to more energy-efficient AI beyond traditional CMOS scaling limits.
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Hybrid Digital-Analog Approximate Inverse Preconditioning for Krylov Methods
Analog-aware block Jacobi schemes in flexible GMRES maintain convergence under simulated device non-idealities when block size, damping, and approximation accuracy are chosen to account for analog scaling, noise, quantization, and clipping.
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Neuromorphic Computing for Low-Power Artificial Intelligence
Neuromorphic computing using compute-in-memory, analog dynamics, and sparse brain-inspired communication offers a route to more energy-efficient AI beyond traditional CMOS scaling limits.