16 citations · 20 across the 12 of their papers we have counts for
13 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…
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…
FeFET-based MirrorBit cell for High-density NVM storage
Paritosh Meihar, Rowtu Srinu, Vivek Saraswat +6
HfO2-based Ferroelectric field-effect transistor (FeFET) has become a center of attraction for non-volatile memory applications because of their low power, fast switching speed, hi…
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…
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…
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…
Simplified Klinokinesis using Spiking Neural Networks for Resource-Constrained Navigation on the Neuromorphic Processor Loihi
Apoorv Kishore, Vivek Saraswat, Udayan Ganguly
C. elegans shows chemotaxis using klinokinesis where the worm senses the concentration based on a single concentration sensor to compute the concentration gradient to perform forag…
Hardware-Friendly Synaptic Orders and Timescales in Liquid State Machines for Speech Classification
Vivek Saraswat, Ajinkya Gorad, Anand Naik +2
Liquid State Machines are brain inspired spiking neural networks (SNNs) with random reservoir connectivity and bio-mimetic neuronal and synaptic models. Reservoir computing network…
Adaptive Chemotaxis for improved Contour Tracking using Spiking Neural Networks
Shashwat Shukla, Rohan Pathak, Vivek Saraswat +1
In this paper we present a Spiking Neural Network (SNN) for autonomous navigation, inspired by the chemotaxis network of the worm Caenorhabditis elegans. In particular, we focus on…
Reaction-Drift Model for Switching Transients in PrCaMnO-Based Resistive RAM
Vivek Saraswat, Shankar Prasad, Abhishek Khanna +5
PrCaMnO (PCMO) based RRAM shows promising memory properties like non-volatility, low variability, multiple resistance states and scalability. From a modeling pe…
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…