26 citations · 67 across the 9 of their papers we have counts for
9 papers
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…
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…
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…
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…
On computational capabilities of Ising machines based on nonlinear oscillators,
Mikhail Erementchouk, Aditya Shukla, Pinaki Mazumder
Dynamical Ising machines are actively investigated from the perspective of finding efficient heuristics for NP-hard optimization problems. However, the existing data demonstrate su…
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…
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…
An On-chip Trainable and Clock-less Spiking Neural Network with 1R Memristive Synapses
Aditya Shukla, Udayan Ganguly
Spiking neural networks (SNNs) are being explored in an attempt to mimic brain's capability to learn and recognize at low power. Crossbar architecture with highly scalable Resistiv…
A Software-equivalent SNN Hardware using RRAM-array for Asynchronous Real-time Learning
Aditya Shukla, Vinay Kumar, Udayan Ganguly
Spiking Neural Network (SNN) naturally inspires hardware implementation as it is based on biology. For learning, spike time dependent plasticity (STDP) may be implemented using an…