190 citations · 715 across the 20 of their papers we have counts for
12 papers · 1 filter
Pruning for Improved ADC Efficiency in Crossbar-based Analog In-memory Accelerators
Timur Ibrayev, Isha Garg, Indranil Chakraborty +1
Deep learning has proved successful in many applications but suffers from high computational demands and requires custom accelerators for deployment. Crossbar-based analog in-memor…
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…
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…
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…
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…
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…