Thomas Van Vaerenbergh

Discretized spin interactions enhance higher-order analog Ising machines

Robbe De Prins, Guy Van der Sande, Peter Bienstman, Thomas Van Vaerenbergh

Continuous-time nonlinear closed-loop in-memory computing for high-accuracy massive MIMO detection

Piergiulio Mannocci, Giacomo Pedretti, Fabian Böhm, Thomas Van Vaerenbergh

Analog in-memory computing (IMC) has emerged as a promising approach for accelerating matrix operations by exploiting the intrinsic physics of memory arrays. To date, however, most IMC architectures have focused on linear algebra workloads in which computation is encoded in the equilibrium …

Parity Check using In Memory Compute ReRAM Macro

F.J.H. de Haas, Fabian Böhm, Dmitri B. Strukov, A.; id_orcid 0000-0001-8573-2910 Todri-Sanial et autres

Hardware-compatible single-shot feasible-space heuristics for solving the quadratic assignment problem

Haesol Im, Chuhong Yang, Moslem Noori, Dmitrii Dobrynin et autres

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A Co-Simulation Framework for Integrated Photonic-Electronic Neuromorphic Computing

Chaerin Hong, Aishwarya Natarajan, Matéj Hejda, Mehmet Berkay On et autres

A practical realization of large-scale integrated spiking neural networks through scalable photonic-electronic neuromorphic architectures requires a robust photonic-electronic simulation framework. Our co-simulation platform integrates both optical components and analog electronic neurons, helping us to accurately validate key performance metrics such as energy …

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Improved Ising formulation of max-3-cut using higher-order spin interactions

Robbe De Prins, Guy Van der Sande, Peter Bienstman, Thomas Van Vaerenbergh

Many combinatorial optimization problems (COPs) are naturally expressed using variables that take on more than two discrete values. To solve such problems using Ising machines (IMs)—specialized analog or digital devices designed to solve COPs efficiently—these multivalued integers must be encoded using binary …

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Robustness-oriented training for automated design of photonic tensor cores

Gianluca Kosmella, Matej Hejda, Thomas Van Vaerenbergh, Wolfger Peelaers

Optical computing aims to leverage the large bandwidth of light and exploits its potential for low-latency signal processing to efficiently handle large-scale Machine Learning (ML) workloads. A critical component of this approach is a Photonic Tensor Core (PTC), which facilitates general matrix-vector …

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Modeling coupled active resonators as an integrated photonic spiking neuron

Mehmet Berkay On, Matej Hejda, Thomas Van Vaerenbergh

Photonic spiking neural networks hold great potential for energy-efficient and high-throughput computation. While many photonic integrated circuits can implement linear synaptic interconnections, the complex dynamics of spiking neurons require careful design considerations under energy consumption, footprint, and integration constraints. Here, we propose …

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Accelerating hybrid XOR–CNF Boolean satisfiability problems natively with in-memory computing

Haesol Im, Fabian Böhm, Giacomo Pedretti, Noriyuki Kushida et autres

The Boolean satisfiability (SAT) problem is a computationally challenging decision problem central to many industrial applications. For SAT problems in cryptanalysis, circuit design, and telecommunication, solutions can often be found more efficiently by representing them with a combination of exclusive OR (XOR) …

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Statistical analysis for per-instance evaluation of stochastic optimizers: Avoiding unreliable conclusions

Moslem Noori, Elisabetta Valiante, Ignacio Rozada, Thomas Van Vaerenbergh et autres

A key trait of stochastic optimizers is that multiple runs of the same optimizer in attempting to solve the same problem can produce different results. As a result, their performance is evaluated over several repeats, or runs, on the problem. However, the …

Dataset for Accelerating Hybrid XOR–CNF SAT Problems Natively with In-Memory Computing

Haesol Im, Fabian Böhm, Giacomo Pedretti, Noriyuki Kushida et autres

This repository contains the instance files used for the paper "Accelerating Hybrid XOR–CNF SAT Problems Natively with In-Memory Computing." The datasets are organized based on their representation (CNF vs. XNF) and whether they have undergone preprocessing. Folder Structure Overview The instances are …

Dataset for Accelerating Hybrid XOR–CNF SAT Problems Natively with In-Memory Computing

Haesol Im, Fabian Böhm, Giacomo Pedretti, Noriyuki Kushida et autres

This repository contains the instance files used for the paper "Accelerating Hybrid XOR–CNF SAT Problems Natively with In-Memory Computing." The datasets are organized based on their representation (CNF vs. XNF) and whether they have undergone preprocessing. Folder Structure Overview The instances are …