597 citations · 4.3k across the 121 of their papers we have counts for
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A Co-design view of Compute in-Memory with Non-Volatile Elements for Neural Networks
Wilfried Haensch, Anand Raghunathan, Kaushik Roy +4
Deep Learning neural networks are pervasive, but traditional computer architectures are reaching the limits of being able to efficiently execute them for the large workloads of tod…
Cryogenic Neuromorphic Hardware
Md Mazharul Islam, Shamiul Alam, Md Shafayat Hossain +2
The revolution in artificial intelligence (AI) brings up an enormous storage and data processing requirement. Large power consumption and hardware overhead have become the main cha…
NAX: Co-Designing Neural Network and Hardware Architecture for Memristive Xbar based Computing Systems
Shubham Negi, Indranil Chakraborty, Aayush Ankit +1
In-Memory Computing (IMC) hardware using Memristive Crossbar Arrays (MCAs) are gaining popularity to accelerate Deep Neural Networks (DNNs) since it alleviates the "memory wall" pr…
On the Intrinsic Robustness of NVM Crossbars Against Adversarial Attacks
Deboleena Roy, Indranil Chakraborty, Timur Ibrayev +1
The increasing computational demand of Deep Learning has propelled research in special-purpose inference accelerators based on emerging non-volatile memory (NVM) technologies. Such…
IMAC: In-memory multi-bit Multiplication andACcumulation in 6T SRAM Array
Mustafa Ali, Akhilesh Jaiswal, Sangamesh Kodge +3
`In-memory computing' is being widely explored as a novel computing paradigm to mitigate the well known memory bottleneck. This emerging paradigm aims at embedding some aspects of…
GENIEx: A Generalized Approach to Emulating Non-Ideality in Memristive Xbars using Neural Networks
Indranil Chakraborty, Mustafa Fayez Ali, Dong Eun Kim +2
The analog nature of computing in Memristive crossbars poses significant issues due to various non-idealities such as: parasitic resistances, non-linear I-V characteristics of the…
sBSNN: Stochastic-Bits Enabled Binary Spiking Neural Network with On-Chip Learning for Energy Efficient Neuromorphic Computing at the Edge
Minsuk Koo, Gopalakrishnan Srinivasan, Yong Shim +1
In this work, we propose stochastic Binary Spiking Neural Network (sBSNN) composed of stochastic spiking neurons and binary synapses (stochastic only during training) that computes…
X-CHANGR: Changing Memristive Crossbar Mapping for Mitigating Line-Resistance Induced Accuracy Degradation in Deep Neural Networks
Amogh Agrawal, Chankyu Lee, Kaushik Roy
There is widespread interest in emerging technologies, especially resistive crossbars for accelerating Deep Neural Networks (DNNs). Resistive crossbars offer a highly-parallel and…
PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference
Aayush Ankit, Izzat El Hajj, Sai Rahul Chalamalasetti +8
Memristor crossbars are circuits capable of performing analog matrix-vector multiplications, overcoming the fundamental energy efficiency limitations of digital logic. They have be…
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…
A Photonic In-Memory Computing primitive for Spiking Neural Networks using Phase-Change Materials
Indranil Chakraborty, Gobinda Saha, Kaushik Roy
Spiking Neural Networks (SNNs) offer an event-driven and more biologically realistic alternative to standard Artificial Neural Networks based on analog information processing. This…
Xcel-RAM: Accelerating Binary Neural Networks in High-Throughput SRAM Compute Arrays
Amogh Agrawal, Akhilesh Jaiswal, Deboleena Roy +4
Deep neural networks are a biologically-inspired class of algorithms that have recently demonstrated state-of-the-art accuracies involving large-scale classification and recognitio…
Exploiting Inherent Error-Resiliency of Neuromorphic Computing to achieve Extreme Energy-Efficiency through Mixed-Signal Neurons
Baibhab Chatterjee, Priyadarshini Panda, Shovan Maity +3
Neuromorphic computing, inspired by the brain, promises extreme efficiency for certain classes of learning tasks, such as classification and pattern recognition. The performance 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…
Design of a Low Voltage Analog-to-Digital Converter using Voltage Controlled Stochastic Switching of Low Barrier Nanomagnets
Indranil Chakraborty, Amogh Agrawal, Kaushik Roy
The inherent stochasticity in many nano-scale devices makes them prospective candidates for low-power computations. Such devices have been demonstrated to exhibit probabilistic swi…
Capacitively Driven Global Interconnect with Magnetoelectric Switching Based Receiver for Higher Energy Efficiency
Zubair Al Azim, Akhilesh Jaiswal, Indranil Chakraborty +1
We propose capacitively driven low-swing global interconnect circuit using a receiver that utilizes magnetoelectric (ME) effect induced magnetization switching to reduce the energy…
8T SRAM Cell as a Multi-bit Dot Product Engine for Beyond von-Neumann Computing
Akhilesh Jaiswal, Indranil Chakraborty, Amogh Agrawal +1
Large scale digital computing almost exclusively relies on the von-Neumann architecture which comprises of separate units for storage and computations. The energy expensive transfe…
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…
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…
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…
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…