16 citations · 20 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
A temporally and spatially local spike-based backpropagation algorithm to enable training in hardware
Anmol Biswas, Vivek Saraswat, Udayan Ganguly
Spiking Neural Networks (SNNs) have emerged as a hardware efficient architecture for classification tasks. The challenge of spike-based encoding has been the lack of a universal tr…
Algorithm For 3D-Chemotaxis Using Spiking Neural Network
Jayesh Choudhary, Vivek Saraswat, Udayan Ganguly
In this work, we aim to devise an end-to-end spiking implementation for contour tracking in 3D media inspired by chemotaxis, where the worm reaches the region which has the given s…
Predicting Performance using Approximate State Space Model for Liquid State Machines
Ajinkya Gorad, Vivek Saraswat, Udayan Ganguly
Liquid State Machine (LSM) is a brain-inspired architecture used for solving problems like speech recognition and time series prediction. LSM comprises of a randomly connected recu…