2 citations · 4 across the 6 of their papers we have counts for
7 papers
Regularization-based Framework for Quantization-, Fault- and Variability-Aware Training
Anmol Biswas, Raghav Singhal, Sivakumar Elangovan +2
Efficient inference is critical for deploying deep learning models on edge AI devices. Low-bit quantization (e.g., 3- and 4-bit) with fixed-point arithmetic improves efficiency, wh…
Temporal and Spatial Reservoir Ensembling Techniques for Liquid State Machines
Anmol Biswas, Sharvari Ashok Medhe, Raghav Singhal +1
Reservoir computing (RC), is a class of computational methods such as Echo State Networks (ESN) and Liquid State Machines (LSM) describe a generic method to perform pattern recogni…
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…
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 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…
Spatially Variant Laplacian Pyramids for Multi-Frame Exposure Fusion
Anmol Biswas, Green Rosh K S, Sachin Deepak Lomte
Laplacian Pyramid Blending is a commonly used method for several seamless image blending tasks. While the method works well for images with comparable intensity levels, it is often…
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…