597 citations · 4.3k across the 121 of their papers we have counts for
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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…
Chaos-guided Input Structuring for Improved Learning in Recurrent Neural Networks
Priyadarshini Panda, Kaushik Roy
Anatomical studies demonstrate that brain reformats input information to generate reliable responses for performing computations. However, it remains unclear how neural circuits en…
X-SRAM: Enabling In-Memory Boolean Computations in CMOS Static Random Access Memories
Amogh Agrawal, Akhilesh Jaiswal, Chankyu Lee +1
Silicon-based Static Random Access Memories (SRAM) and digital Boolean logic have been the workhorse of the state-of-art computing platforms. Despite tremendous strides in scaling…
Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing
Syed Shakib Sarwar, Aayush Ankit, Kaushik Roy
Deep convolutional neural network (DCNN) based supervised learning is a widely practiced approach for large-scale image classification. However, retraining these large networks to…
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…
Technology Aware Training in Memristive Neuromorphic Systems based on non-ideal Synaptic Crossbars
Indranil Chakraborty, Deboleena Roy, Kaushik Roy
The advances in the field of machine learning using neuromorphic systems have paved the pathway for extensive research on possibilities of hardware implementations of neural networ…
SPARE: Spiking Networks Acceleration Using CMOS ROM-Embedded RAM as an In-Memory-Computation Primitive
Amogh Agrawal, Aayush Ankit, Kaushik Roy
Despite huge success of artificial intelligence, hardware systems running these algorithms consume orders of magnitude higher energy compared to the human brain, mainly due to heav…
An Energy-Efficient Mixed-Signal Neuron for Inherently Error-Resilient Neuromorphic Systems
Baibhab Chatterjee, Priyadarshini Panda, Shovan Maity +2
This work presents the design and analysis of a mixed-signal neuron (MS-N) for convolutional neural networks (CNN) and compares its performance with a digital neuron (Dig-N) in ter…
STDP Based Pruning of Connections and Weight Quantization in Spiking Neural Networks for Energy Efficient Recognition
Nitin Rathi, Priyadarshini Panda, Kaushik Roy
Spiking Neural Networks (SNNs) with a large number of weights and varied weight distribution can be difficult to implement in emerging in-memory computing hardware due to the limit…
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,…
Gabor Filter Assisted Energy Efficient Fast Learning Convolutional Neural Networks
Syed Shakib Sarwar, Priyadarshini Panda, Kaushik Roy
Convolutional Neural Networks (CNN) are being increasingly used in computer vision for a wide range of classification and recognition problems. However, training these large networ…
Voltage-Driven Domain-Wall Motion based Neuro-Synaptic Devices for Dynamic On-line Learning
Akhilesh Jaiswal, Amogh Agrawal, Priyadarshini Panda +1
Conventional von-Neumann computing models have achieved remarkable feats for the past few decades. However, they fail to deliver the required efficiency for certain basic tasks lik…
Magnetic Skyrmions for Cache Memory
Mei-Chin Chen, Kaushik Roy
Magnetic skyrmions (MS) are particle-like spin structures with whirling configuration, which are promising candidates for spin-based memory. MS contains alluring features including…
Convolutional Spike Timing Dependent Plasticity based Feature Learning in Spiking Neural Networks
Priyadarshini Panda, Gopalakrishnan Srinivasan, Kaushik Roy
Brain-inspired learning models attempt to mimic the cortical architecture and computations performed in the neurons and synapses constituting the human brain to achieve its efficie…
Perovskite Quantum Organismoids
Fan Zuo, Priyadarshini Panda, Michele Kotiuga +14
A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making1-3. This behavior, known as h…
ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural Networks
Priyadarshini Panda, Jason M. Allred, Shriram Ramanathan +1
A fundamental feature of learning in animals is the "ability to forget" that allows an organism to perceive, model and make decisions from disparate streams of information and adap…
Computing in Memory with Spin-Transfer Torque Magnetic RAM
Shubham Jain, Ashish Ranjan, Kaushik Roy +1
In-memory computing is a promising approach to addressing the processor-memory data transfer bottleneck in computing systems. We propose Spin-Transfer Torque Compute-in-Memory (STT…
Extent of Variation Resilience in Strained CMOS: From Transistors to Digital Circuits
Ahmad Ehteshamul Islam, Charles Augustine, Kaushik Roy +1
Process-related and stress-induced changes in threshold voltage are major variability concerns in ultra-scaled CMOS transistors. The device designers consider this variability as a…
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…
Energy-Efficient Memories using Magneto-Electric Switching of Ferromagnets
Akhilesh Jaiswal, Indranil Chakraborty, Kaushik Roy
Voltage driven magneto-electric (ME) switching of ferro-magnets has shown potential for future low-energy spintronic memories. In this paper, we first analyze two different ME devi…