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20172025
most citedA Software-equivalent SNN Hardware using RRAM-array for Asynchronous Real-time Learning

26 citations · 67 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.ET2025

Relaxation-based dynamical Ising machines for discrete tomography

Mikhail Erementchouk, Aditya Shukla, Pinaki Mazumder

Dynamical Ising machines are continuous dynamical systems that evolve from a generic initial state to a state strongly related to the ground state of the classical Ising model. We…

cs.ET2024★ 3 cited

Non-binary dynamical Ising machines for combinatorial optimization

Aditya Shukla, Mikhail Erementchouk, Pinaki Mazumder

Dynamical Ising machines achieve accelerated solving of complex combinatorial optimization problems by remapping the convergence to the ground state of the classical spin networks…

cs.ET2023★ 1 cited

Self-contained relaxation-based dynamical Ising machines

Mikhail Erementchouk, Aditya Shukla, Pinaki Mazumder

Dynamical Ising machines are based on continuous dynamical systems evolving from a generic initial state to a state strongly related to the ground state of the classical Ising mode…

cs.ET2022★ 1 cited

Scalable almost-linear dynamical Ising machines

Aditya Shukla, Mikhail Erementchouk, Pinaki Mazumder

The past decade has seen the emergence of Ising machines targeting hard combinatorial optimization problems by minimizing the Ising Hamiltonian with spins represented by continuous…

cs.ET2020

Linear Delay-cell Design for Low-energy Delay Multiplication and Accumulation

Aditya Shukla

A practical deep neural network's (DNN) evaluation involves thousands of multiply-and-accumulate (MAC) operations. To extend DNN's superior inference capabilities to energy constra…

cs.ET2018

A case for multiple and parallel RRAMs as synaptic model for training SNNs

Aditya Shukla, Sidharth Prasad, Sandip Lashkare +1

To enable a dense integration of model synapses in a spiking neural networks hardware, various nano-scale devices are being considered. Such a device, besides exhibiting spike-time…