57 citations · 183 across the 33 of their papers we have counts for
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Schottky Barrier MOSFET Enabled Ultra-Low Power Real-Time Neuron for Neuromorphic Computing
Shubham Patil, Jayatika Sakhuja, Ajay Kumar Singh +5
Energy-efficient real-time synapses and neurons are essential to enable large-scale neuromorphic computing. In this paper, we propose and demonstrate the Schottky-Barrier MOSFET-ba…
Stochasticity Invariance Control in PrCaMnO RRAM to enable Large-Scale Stochastic Recurrent Neural Networks
Vivek Saraswat, Udayan Ganguly
Emerging non-volatile memories have been proposed for a wide range of applications from easing the von-Neumann bottleneck to neuromorphic applications. Specifically, scalable RRAMs…
Exploiting the Electrothermal Timescale in PrMnO3 RRAM for a compact, clock-less neuron exhibiting biological spiking patterns
Omkar Phadke, Jayatika Sakhuja, Vivek Saraswat +1
Spiking Neural Networks (SNNs) are gaining widespread momentum in the field of neuromorphic computing. These network systems integrated with neurons and synapses provide computatio…
Ultra-low Energy charge trap flash based synapse enabled by parasitic leakage mitigation
Shalini Shrivastava, Tanmay Chavan, Udayan Ganguly
Brain-inspired computation promises complex cognitive tasks at biological energy efficiencies. The brain contains synapses per neuron. Hence, ultra-low energy, high-density…
A case for multiple and parallel RRAMs as synaptic model for training SNNs
Aditya Shukla, Sidharth Prasad, Sandip Lashkare +1
To enable a dense integration of model synapses in a spiking neural networks hardware, various nano-scale devices are being considered. Such a device, besides exhibiting spike-time…
Enhanced Circuit Densities in Epitaxially Defined FinFETs (EDFinFETs) over FinFETs
Sushant Mittal, Aneesh Nainani, M. C. Abraham +2
FinFET technology is prone to suffer from Line Edge Roughness (LER) based VT variation with scaling. To address this, we proposed an Epitaxially Defined (ED) FinFET (EDFinFET) as a…