2 citations · 3 across the 4 of their papers we have counts for
5 papers
System-level Impact of Non-Ideal Program-Time of Charge Trap Flash (CTF) on Deep Neural Network
S. Shrivastava, A. Biswas, S. Chakrabarty +3
Learning of deep neural networks (DNN) using Resistive Processing Unit (RPU) architecture is energy-efficient as it utilizes dedicated neuromorphic hardware and stochastic computat…
Non-Ideal Program-Time Conservation in Charge Trap Flash for Deep Learning
Shalini Shrivastava, Vivek Saraswat, Gayatri Dash +2
Training deep neural networks (DNNs) is computationally intensive but arrays of non-volatile memories like Charge Trap Flash (CTF) can accelerate DNN operations using in-memory com…
Evolution of ferroelectricity with annealing temperature and thickness in sputter deposited undoped HfO on silicon
Md Hanif Ali, Adityanarayan Pandey, Rowtu Srinu +4
Ferroelectricity in sputtered undoped-HfO is attractive for composition control for low power and non-volatile memory and logic applications. Unlike doped HfO, evolution of…
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…
A simple and efficient SNN and its performance & robustness evaluation method to enable hardware implementation
Anmol Biswas, Sidharth Prasad, Sandip Lashkare +1
Spiking Neural Networks (SNN) are more closely related to brain-like computation and inspire hardware implementation. This is enabled by small networks that give high performance o…