918 citations · 2.5k across the 22 of their papers we have counts for
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SpinAPS: A High-Performance Spintronic Accelerator for Probabilistic Spiking Neural Networks
Anakha V Babu, Osvaldo Simeone, Bipin Rajendran
We discuss a high-performance and high-throughput hardware accelerator for probabilistic Spiking Neural Networks (SNNs) based on Generalized Linear Model (GLM) neurons, that uses b…
Memristors -- from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing
Adnan Mehonic, Abu Sebastian, Bipin Rajendran +3
Machine learning, particularly in the form of deep learning, has driven most of the recent fundamental developments in artificial intelligence. Deep learning is based on computatio…
ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning
Vinay Joshi, Geethan Karunaratne, Manuel Le Gallo +5
Deep neural networks (DNNs) have surpassed human-level accuracy in a variety of cognitive tasks but at the cost of significant memory/time requirements in DNN training. This limits…
Mixed-precision deep learning based on computational memory
S. R. Nandakumar, Manuel Le Gallo, Christophe Piveteau +11
Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition…
Bio-mimetic Synaptic Plasticity and Learning in a sub-500mV Cu/SiO/W Memristor
S. R. Nandakumar, Bipin Rajendran
The computational efficiency of the human brain is believed to stem from the parallel information processing capability of neurons with integrated storage in synaptic interconnecti…