57 citations · 183 across the 33 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…
An On-chip Trainable and Clock-less Spiking Neural Network with 1R Memristive Synapses
Aditya Shukla, Udayan Ganguly
Spiking neural networks (SNNs) are being explored in an attempt to mimic brain's capability to learn and recognize at low power. Crossbar architecture with highly scalable Resistiv…
A Software-equivalent SNN Hardware using RRAM-array for Asynchronous Real-time Learning
Aditya Shukla, Vinay Kumar, Udayan Ganguly
Spiking Neural Network (SNN) naturally inspires hardware implementation as it is based on biology. For learning, spike time dependent plasticity (STDP) may be implemented using an…
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…