203 citations · 1.3k across the 57 of their papers we have counts for
33 papers · 1 filter
STAR: Astrocyte-Inspired State-Augmented Repair for Supervised Memristive AI Hardware Systems
Yusuf Ahmed Khan, Zhuangyu Han, Abhronil Sengupta
Memristive crossbar arrays have emerged as a promising platform for efficient on-chip learning, enabling local learning rules such as Equilibrium Propagation (EP) to be realized wi…
Bayesian Optimization of Crossbar-Based Compute-In-Memory System Design for Efficient DNN Inference
Arnob Saha, Bibhas Manna, Nikhil Kotikalapudi +4
Leveraging the high density and energy efficiency of Compute-In-Memory (CIM) crossbar-based Deep Neural Network (DNN) accelerators requires optimal Design Space Exploration (DSE),…
StochEP: Stochastic Equilibrium Propagation for Spiking Convergent Recurrent Neural Networks
Jiaqi Lin, Yi Jiang, Abhronil Sengupta
Spiking Neural Networks (SNNs) promise energy-efficient, sparse, biologically inspired computation. Training them with Backpropagation Through Time (BPTT) and surrogate gradients a…
Dendritic Computing with Multi-Gate Ferroelectric Field-Effect Transistors
A N M Nafiul Islam, Xuezhong Niu, Jiahui Duan +3
Although inspired by neuronal systems in the brain, artificial neural networks generally employ point-neurons, which offer far less computational complexity than their biological c…
A Bio-inspired Asymmetric Double-Gate Ferroelectric FET for Emulating Astrocyte and Dendrite Dynamics in Neuromorphic Systems
Zhouhang Jiang, A N M Nafiul Islam, Zhuangyu Han +13
Neuromorphic systems seek to replicate the functionalities of biological neural networks to attain significant improvements in performance and efficiency of AI computing platforms.…
An In-Situ Spatial-Temporal Sequence Detector for Neuromorphic Vision Sensor Empowered by High Density Vertical NAND Storage
Zijian Zhao, Varun Darshana Parekh, Po-Kai Hsu +17
Neuromorphic vision sensors require efficient real-time pattern recognition, yet conventional architectures struggle with energy and latency constraints. Here, we present a novel i…
Variation-Resilient FeFET-Based In-Memory Computing Leveraging Probabilistic Deep Learning
Bibhas Manna, Arnob Saha, Zhouhang Jiang +2
Reliability issues stemming from device level non-idealities of non-volatile emerging technologies like ferroelectric field-effect transistors (FeFET), especially at scaled dimensi…
Hardware in Loop Learning with Spin Stochastic Neurons
A N M Nafiul Islam, Kezhou Yang, Amit K. Shukla +4
Despite the promise of superior efficiency and scalability, real-world deployment of emerging nanoelectronic platforms for brain-inspired computing have been limited thus far, prim…
Leveraging Probabilistic Switching in Superparamagnets for Temporal Information Encoding in Neuromorphic Systems
Kezhou Yang, Dhuruva Priyan G M, Abhronil Sengupta
Brain-inspired computing - leveraging neuroscientific principles underpinning the unparalleled efficiency of the brain in solving cognitive tasks - is emerging to be a promising pa…
Hybrid Stochastic Synapses Enabled by Scaled Ferroelectric Field-effect Transistors
A N M Nafiul Islam, Arnob Saha, Zhouhang Jiang +2
Achieving brain-like density and performance in neuromorphic computers necessitates scaling down the size of nanodevices emulating neuro-synaptic functionalities. However, scaling…
Leveraging Voltage-Controlled Magnetic Anisotropy to Solve Sneak Path Issues in Crossbar Arrays
Kezhou Yang, Abhronil Sengupta
In crossbar array structures, which serves as an "In-Memory" compute engine for Artificial Intelligence hardware, write sneak path problem causes undesired switching of devices tha…
Intrinsic synaptic plasticity of ferroelectric field effect transistors for online learning
Arnob Saha, A N M Nafiul Islam, Zijian Zhao +3
Nanoelectronic devices emulating neuro-synaptic functionalities through their intrinsic physics at low operating energies is imperative toward the realization of brain-like neuromo…
Emulation of Astrocyte Induced Neural Phase Synchrony in Spin-Orbit Torque Oscillator Neurons
Umang Garg, Kezhou Yang, Abhronil Sengupta
Astrocytes play a central role in inducing concerted phase synchronized neural-wave patterns inside the brain. In this article, we demonstrate that injected radio-frequency signal…
Stochastic Magnetoelectric Neuron for Temporal Information Encoding
Kezhou Yang, Abhronil Sengupta
Emulating various facets of computing principles of the brain can potentially lead to the development of neuro-computers that are able to exhibit brain-like cognitive capabilities.…
Exploiting Oxide Based Resistive RAM Variability for Bayesian Neural Network Hardware Design
Akul Malhotra, Sen Lu, Kezhou Yang +1
Uncertainty plays a key role in real-time machine learning. As a significant shift from standard deep networks, which does not consider any uncertainty formulation during its train…
All-Spin Bayesian Neural Networks
Kezhou Yang, Akul Malhotra, Sen Lu +1
Probabilistic machine learning enabled by the Bayesian formulation has recently gained significant attention in the domain of automated reasoning and decision-making. While impress…
RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars
Shubham Jain, Abhronil Sengupta, Kaushik Roy +1
Resistive crossbars designed with non-volatile memory devices have emerged as promising building blocks for Deep Neural Network (DNN) hardware, due to their ability to compactly an…
Toward Fast Neural Computing using All-Photonic Phase Change Spiking Neurons
Indranil Chakraborty, Gobinda Saha, Abhronil Sengupta +1
The rapid growth of brain-inspired computing coupled with the inefficiencies in the CMOS implementations of neuromrphic systems has led to intense exploration of efficient hardware…
Encoding Neural and Synaptic Functionalities in Electron Spin: A Pathway to Efficient Neuromorphic Computing
Abhronil Sengupta, Kaushik Roy
Present day computers expend orders of magnitude more computational resources to perform various cognitive and perception related tasks that humans routinely perform everyday. This…
An All-Memristor Deep Spiking Neural Computing System: A Step Towards Realizing the Low Power,Stochastic Brain
Parami Wijesinghe, Aayush Ankit, Abhronil Sengupta +1
Deep 'Analog Artificial Neural Networks' (ANNs) perform complex classification problems with remarkably high accuracy. However, they rely on humongous amount of power to perform th…
Stochastic Spiking Neural Networks Enabled by Magnetic Tunnel Junctions: From Nontelegraphic to Telegraphic Switching Regimes
Chamika M. Liyanagedera, Abhronil Sengupta, Akhilesh Jaiswal +1
Stochastic spiking neural networks based on nanoelectronic spin devices can be a possible pathway to achieving "brainlike" compact and energy-effcient cognitive intelligence. The c…
TraNNsformer: Neural network transformation for memristive crossbar based neuromorphic system design
Aayush Ankit, Abhronil Sengupta, Kaushik Roy
Implementation of Neuromorphic Systems using post Complementary Metal-Oxide-Semiconductor (CMOS) technology based Memristive Crossbar Array (MCA) has emerged as a promising solutio…
Stochastic Spin-Orbit Torque Devices as Elements for Bayesian Inference
Yong Shim, Shuhan Chen, Abhronil Sengupta +1
Probabilistic inference from real-time input data is becoming increasingly popular and may be one of the potential pathways at enabling cognitive intelligence. As a matter of fact,…
RESPARC: A Reconfigurable and Energy-Efficient Architecture with Memristive Crossbars for Deep Spiking Neural Networks
Aayush Ankit, Abhronil Sengupta, Priyadarshini Panda +1
Neuromorphic computing using post-CMOS technologies is gaining immense popularity due to its promising abilities to address the memory and power bottlenecks in von-Neumann computin…