83 citations · 309 across the 36 of their papers we have counts for
37 papers
Brain-Inspired Spiking Neural Network for Online Unsupervised Time Series Prediction
Biswadeep Chakraborty, Saibal Mukhopadhyay
Energy and data-efficient online time series prediction for predicting evolving dynamical systems are critical in several fields, especially edge AI applications that need to updat…
Unsupervised 3D Object Learning through Neuron Activity aware Plasticity
Beomseok Kang, Biswadeep Chakraborty, Saibal Mukhopadhyay
We present an unsupervised deep learning model for 3D object classification. Conventional Hebbian learning, a well-known unsupervised model, suffers from loss of local features lea…
Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles
Biswadeep Chakraborty, Saibal Mukhopadhyay
This paper shows that the heterogeneity in neuronal and synaptic dynamics reduces the spiking activity of a Recurrent Spiking Neural Network (RSNN) while improving prediction perfo…
Roadmap for Unconventional Computing with Nanotechnology
Giovanni Finocchio, Jean Anne C. Incorvia, Joseph S. Friedman +48
In the "Beyond Moore's Law" era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same ti…
Heterogeneous Recurrent Spiking Neural Network for Spatio-Temporal Classification
Biswadeep Chakraborty, Saibal Mukhopadhyay
Spiking Neural Networks are often touted as brain-inspired learning models for the third wave of Artificial Intelligence. Although recent SNNs trained with supervised backpropagati…
Forecasting Local Behavior of Self-organizing Many-agent System without Reconstruction
Beomseok Kang, Minah Lee, Harshit Kumar +1
Large multi-agent systems are often driven by locally defined agent interactions, which is referred to as self-organization. Our primary objective is to determine when the propagat…
Forecasting Evolution of Clusters in Game Agents with Hebbian Learning
Beomseok Kang, Saibal Mukhopadhyay
Large multi-agent systems such as real-time strategy games are often driven by collective behavior of agents. For example, in StarCraft II, human players group spatially near agent…
An Algorithm-Hardware Co-design Framework to Overcome Imperfections of Mixed-signal DNN Accelerators
Payman Behnam, Uday Kamal, Saibal Mukhopadhyay
In recent years, processing in memory (PIM) based mixedsignal designs have been proposed as energy- and area-efficient solutions with ultra high throughput to accelerate DNN comput…
Learning Point Processes using Recurrent Graph Network
Saurabh Dash, Xueyuan She, Saibal Mukhopadhyay
We present a novel Recurrent Graph Network (RGN) approach for predicting discrete marked event sequences by learning the underlying complex stochastic process. Using the framework…
Unsupervised Hebbian Learning on Point Sets in StarCraft II
Beomseok Kang, Harshit Kumar, Saurabh Dash +1
Learning the evolution of real-time strategy (RTS) game is a challenging problem in artificial intelligent (AI) system. In this paper, we present a novel Hebbian learning method to…
Radar Guided Dynamic Visual Attention for Resource-Efficient RGB Object Detection
Hemant Kumawat, Saibal Mukhopadhyay
An autonomous system's perception engine must provide an accurate understanding of the environment for it to make decisions. Deep learning based object detection networks experienc…
XMD: An Expansive Hardware-telemetry based Mobile Malware Detector to enhance Endpoint Detection
Harshit Kumar, Biswadeep Chakraborty, Sudarshan Sharma +1
Hardware-based Malware Detectors (HMDs) have shown promise in detecting malicious workloads. However, the current HMDs focus solely on the CPU core of a System-on-Chip (SoC) and, t…
RADNet: A Deep Neural Network Model for Robust Perception in Moving Autonomous Systems
Burhan A. Mudassar, Sho Ko, Maojingjing Li +2
Interactive autonomous applications require robustness of the perception engine to artifacts in unconstrained videos. In this paper, we examine the effect of camera motion on the t…
Unraveled Multilevel Transformation Networks for Predicting Sparsely-Observed Spatiotemporal Dynamics
Priyabrata Saha, Saibal Mukhopadhyay
In this paper, we address the problem of predicting complex, nonlinear spatiotemporal dynamics when available data is recorded at irregularly-spaced sparse spatial locations. Most…
DARTS: Model Uncertainty-Aware Differentiable Architecture Search
Biswadeep Chakraborty, Saibal Mukhopadhyay
We present a Model Uncertainty-aware Differentiable ARchiTecture Search (DARTS) that optimizes neural networks to simultaneously achieve high accuracy and low uncertainty. We in…
Characterization of Generalizability of Spike Timing Dependent Plasticity trained Spiking Neural Networks
Biswadeep Chakraborty, Saibal Mukhopadhyay
A Spiking Neural Network (SNN) is trained with Spike Timing Dependent Plasticity (STDP), which is a neuro-inspired unsupervised learning method for various machine learning applica…
A Quantum Hopfield Associative Memory Implemented on an Actual Quantum Processor
Nathan Eli Miller, Saibal Mukhopadhyay
In this work, we present a Quantum Hopfield Associative Memory (QHAM) and demonstrate its capabilities in simulation and hardware using IBM Quantum Experience. The QHAM is based on…
A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection
Biswadeep Chakraborty, Xueyuan She, Saibal Mukhopadhyay
This paper proposes a Fully Spiking Hybrid Neural Network (FSHNN) for energy-efficient and robust object detection in resource-constrained platforms. The network architecture is ba…
Towards Improving the Trustworthiness of Hardware based Malware Detector using Online Uncertainty Estimation
Harshit Kumar, Nikhil Chawla, Saibal Mukhopadhyay
Hardware-based Malware Detectors (HMDs) using Machine Learning (ML) models have shown promise in detecting malicious workloads. However, the conventional black-box based machine le…
A Deep Learning Approach for Predicting Spatiotemporal Dynamics From Sparsely Observed Data
Priyabrata Saha, Saibal Mukhopadhyay
In this paper, we consider the problem of learning prediction models for spatiotemporal physical processes driven by unknown partial differential equations (PDEs). We propose a dee…
Neural Identification for Control
Priyabrata Saha, Magnus Egerstedt, Saibal Mukhopadhyay
We present a new method for learning control law that stabilizes an unknown nonlinear dynamical system at an equilibrium point. We formulate a system identification task in a self-…
Physics-Incorporated Convolutional Recurrent Neural Networks for Source Identification and Forecasting of Dynamical Systems
Priyabrata Saha, Saurabh Dash, Saibal Mukhopadhyay
Spatio-temporal dynamics of physical processes are generally modeled using partial differential equations (PDEs). Though the core dynamics follows some principles of physics, real-…
Low Power Unsupervised Anomaly Detection by Non-Parametric Modeling of Sensor Statistics
Ahish Shylendra, Priyesh Shukla, Saibal Mukhopadhyay +2
This work presents AEGIS, a novel mixed-signal framework for real-time anomaly detection by examining sensor stream statistics. AEGIS utilizes Kernel Density Estimation (KDE)-based…
MagNet: Discovering Multi-agent Interaction Dynamics using Neural Network
Priyabrata Saha, Arslan Ali, Burhan A. Mudassar +2
We present the MagNet, a neural network-based multi-agent interaction model to discover the governing dynamics and predict evolution of a complex multi-agent system from observatio…