57 citations · 183 across the 33 of their papers we have counts for
34 papers
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…
Ferroelectric MirrorBit-Integrated Field-Programmable Memory Array for TCAM, Storage, and In-Memory Computing Applications
Paritosh Meihar, Rowtu Srinu, Sandip Lashkare +6
In-memory computing on a reconfigurable architecture is the emerging field which performs an application-based resource allocation for computational efficiency and energy optimizat…
Process Voltage Temperature Variability Estimation of Tunneling Current for Band-to-Band-Tunneling based Neuron
Shubham Patil, Anand Sharma, Gaurav R +5
Compact and energy-efficient Synapse and Neurons are essential to realize the full potential of neuromorphic computing. In addition, a low variability is indeed needed for neurons…
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…
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…
Interlayer-engineered local epitaxial templating induced enhancement in polarization (2P > 70C/cm) in HfZrO thin films
Srinu Rowtu, Paritosh Meihar, Adityanarayan Pandey +3
In this work, we report a high remnant polarization, 2Pr >70C/cm in thermally processed atomic layer deposited Hf0.5Zr0.5O2 (HZO) film on Silicon with NH3 plasma exposed thi…
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…
An Accurate Process Induced Variability Aware Compact Model-based Circuit Performance Estimation for Design-Technology Co-optimization
Shubham Patil, Amita Rawat, Udayan Ganguly
In sub-10nm FinFETs, Line-edge-roughness (LER) and metal-gate granularity (MGG) are the two most dominant sources of variability and are mostly modeled semi-empirically. In this wo…
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…
Spiking-GAN: A Spiking Generative Adversarial Network Using Time-To-First-Spike Coding
Vineet Kotariya, Udayan Ganguly
Spiking Neural Networks (SNNs) have shown great potential in solving deep learning problems in an energy-efficient manner. However, they are still limited to simple classification…
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…
India's Rise in Nanoelectronics Research
Udayan Ganguly, Sandip Lashkare, Swaroop Ganguly
Modern semiconductors innovation has a strong relation to scale and skill. While India has a significant demand for semiconductors, it has a daunting challenge to create a semicond…
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…
Investigating Transient Characteristics of Volatile Hysteresis and Self-Heating of PrMnO based RRAM
J. Sakhuja, S. Lashkare, K. Jana +1
PrMnO (PMO) based RRAM shows selector-less behavior due to high-non-linearity. Recently, the non-linearity, along with volatile hysteresis, is demonstrated and utilized as a co…
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…
Understanding the Location of Resistance Change in the Pr0.7Ca0.3MnO3 RRAM
Sandip Lashkare, Udayan Ganguly
Pr1-xCaxMnO3 (PCMO) based resistance random access memory (RRAM) is attractive in large scale memory and neuromorphic applications as it is non-filamentary, area scalable and has m…
Software-Level Accuracy Using Stochastic Computing With Charge-Trap-Flash Based Weight Matrix
Varun Bhatt, Shalini Shrivastava, Tanmay Chavan +1
The in-memory computing paradigm with emerging memory devices has been recently shown to be a promising way to accelerate deep learning. Resistive processing unit (RPU) has been pr…
Structured Mean-field Variational Inference and Learning in Winner-take-all Spiking Neural Networks
Shashwat Shukla, Hideaki Shimazaki, Udayan Ganguly
The Bayesian view of the brain hypothesizes that the brain constructs a generative model of the world, and uses it to make inferences via Bayes' rule. Although many types of approx…
Band-to-Band Tunneling based Ultra-Energy Efficient Silicon Neuron
Tanmay Chavan, Sangya Dutta, Nihar R. Mohapatra +1
The human brain comprises about a hundred billion neurons connected through quadrillion synapses. Spiking Neural Networks (SNNs) take inspiration from the brain to model complex co…
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…