918 citations · 2.5k across the 22 of their papers we have counts for
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Mixed-precision training of deep neural networks using computational memory
Nandakumar S. R., Manuel Le Gallo, Irem Boybat +3
Deep neural networks have revolutionized the field of machine learning by providing unprecedented human-like performance in solving many real-world problems such as image and speec…
Stochastic Deep Learning in Memristive Networks
Anakha V Babu, Bipin Rajendran
We study the performance of stochastically trained deep neural networks (DNNs) whose synaptic weights are implemented using emerging memristive devices that exhibit limited dynamic…
Learning and Real-time Classification of Hand-written Digits With Spiking Neural Networks
Shruti R. Kulkarni, John M. Alexiades, Bipin Rajendran
We describe a novel spiking neural network (SNN) for automated, real-time handwritten digit classification and its implementation on a GP-GPU platform. Information processing withi…
Neuromorphic computing with multi-memristive synapses
Irem Boybat, Manuel Le Gallo, S. R. Nandakumar +7
Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which…
Training Probabilistic Spiking Neural Networks with First-to-spike Decoding
Alireza Bagheri, Osvaldo Simeone, Bipin Rajendran
Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at harnessing the energy efficiency of spike-domain processing by building on computing elements that opera…