294 citations · 414 across the 9 of their papers we have counts for
3 papers · 1 filter
Kernel Approximation using Analog In-Memory Computing
Julian Büchel, Giacomo Camposampiero, Athanasios Vasilopoulos +4
Kernel functions are vital ingredients of several machine learning algorithms, but often incur significant memory and computational costs. We introduce an approach to kernel approx…
Network insensitivity to parameter noise via adversarial regularization
Julian Büchel, Fynn Faber, Dylan R. Muir
Neuromorphic neural network processors, in the form of compute-in-memory crossbar arrays of memristors, or in the form of subthreshold analog and mixed-signal ASICs, promise enormo…
Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors
Julian Büchel, Dmitrii Zendrikov, Sergio Solinas +2
Mixed-signal analog/digital circuits emulate spiking neurons and synapses with extremely high energy efficiency, an approach known as "neuromorphic engineering". However, analog ci…