22 citations · 39 across the 6 of their papers we have counts for
7 papers
Accelerating Hybrid XORCNF Boolean Satisfiability Problems Natively with In-Memory Computing
Haesol Im, Fabian Böhm, Giacomo Pedretti +14
The Boolean satisfiability (SAT) problem is a computationally challenging decision problem central to many industrial applications. For SAT problems in cryptanalysis, circuit desig…
Solving Boolean satisfiability problems with resistive content addressable memories
Giacomo Pedretti, Fabian Böhm, Tinish Bhattacharya +15
Solving optimization problems is a highly demanding workload requiring high-performance computing systems. Optimization solvers are usually difficult to parallelize in conventional…
Computing High-Degree Polynomial Gradients in Memory
T. Bhattacharya, G. H. Hutchinson, G. Pedretti +6
Specialized function gradient computing hardware could greatly improve the performance of state-of-the-art optimization algorithms, e.g., based on gradient descent or conjugate gra…
FPIA: Field-Programmable Ising Arrays with In-Memory Computing
George Higgins Hutchinson, Ethan Sifferman, Tinish Bhattacharya +1
Ising Machine is a promising computing approach for solving combinatorial optimization problems. It is naturally suited for energy-saving and compact in-memory computing implementa…
Memristor-based hardware and algorithms for higher-order Hopfield optimization solver outperforming quadratic Ising machines
Mohammad Hizzani, Arne Heittmann, George Hutchinson +6
Ising solvers offer a promising physics-based approach to tackle the challenging class of combinatorial optimization problems. However, typical solvers operate in a quadratic energ…
Low-Power (1T1N) Skyrmionic Synapses for Spiking Neuromorphic Systems
Tinish Bhattacharya, Sai Li, Yangqi Huang +3
In this work, we propose a `1-transistor 1-nanotrack' (1T1N) synapse based on movement of magnetic skyrmions using spin polarised current pulses. The proposed synaptic bit-cell has…
MASTISK
Tinish Bhattacharya, Vivek Parmar, Manan Suri
In this paper, we present MASTISK (MAchine-learning and Synaptic-plasticity Technology Integrated Simulation frameworK). MASTISK is an open-source versatile and flexible tool devel…