92 citations · 248 across the 16 of their papers we have counts for
17 papers
Continuous-time nonlinear closed-loop in-memory computing for high-accuracy massive MIMO detection
Piergiulio Mannocci, Giacomo Pedretti, Fabian Böhm +1
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…
Modeling the Quantum Photon Statistics in Hybrid Light-Matter Integrated Circuits
Mathias Van Regemortel, Vincenzo Ardizzone, Eugenio Maggiolini +3
Strong light-matter coupling between a guided electromagnetic mode and an excitonic semiconductor transition gives rise to exciton-polaritons with optical nonlinearities far exceed…
Optimizing Quantum Photonic Integrated Circuits using Differentiable Tensor Networks
Mathias Van Regemortel, Thomas Van Vaerenbergh
Recent reports of large photonic nonlinearities in integrated photonic devices, using the strong excitonic light-matter coupling in semiconductors, necessitate a tailored design fr…
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…
Hardware-Compatible Single-Shot Feasible-Space Heuristics for Solving the Quadratic Assignment Problem
Haesol Im, Chan-Woo Yang, Moslem Noori +10
Research into the development of special-purpose computing architectures designed to solve quadratic unconstrained binary optimization (QUBO) problems has flourished in recent year…
Parallel Tempering-Inspired Distributed Binary Optimization with In-Memory Computing
Xiangyi Zhang, Fabian Böhm, Elisabetta Valiante +7
In-memory computing (IMC) has been shown to be a promising approach for solving binary optimization problems while significantly reducing energy and latency. Building on the advant…
Invited: Neuromorphic architectures based on augmented silicon photonics platforms
Matěj Hejda, Federico Marchesin, George Papadimitriou +6
In this work, we discuss our vision for neuromorphic accelerators based on integrated photonics within the framework of the Horizon Europe NEUROPULS project. Augmented integrated p…
Separable Operator Networks
Xinling Yu, Sean Hooten, Ziyue Liu +4
Operator learning has become a powerful tool in machine learning for modeling complex physical systems governed by partial differential equations (PDEs). Although Deep Operator Net…
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…
Automatic differentiation accelerated shape optimization approaches to photonic inverse design on rectilinear simulation grids
Sean Hooten, Peng Sun, Liron Gantz +3
Shape optimization approaches to inverse design offer low-dimensional, physically-guided parameterizations of structures by representing them as combinations of shape primitives. H…
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…
Adjoint optimization of polarization-splitting grating couplers
Peng Sun, Thomas Van Vaerenbergh, Sean Hooten +1
We have designed a polarization-splitting grating coupler (PSGC) in silicon-oninsulator (SOI) that has 1.2 dB peak loss in numerical simulations, which is the best simulated perfor…
The Boundary for Quantum Advantage in Gaussian Boson Sampling
Jacob F. F. Bulmer, Bryn A. Bell, Rachel S. Chadwick +9
Identifying the boundary beyond which quantum machines provide a computational advantage over their classical counterparts is a crucial step in charting their usefulness. Gaussian…
Inverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning
Sean Hooten, Raymond G. Beausoleil, Thomas Van Vaerenbergh
We present a proof-of-concept technique for the inverse design of electromagnetic devices motivated by the policy gradient method in reinforcement learning, named PHORCED (PHotonic…
Thermally tunable hybrid photonic architecture for nonlinear optical circuits
Marina Radulaski, Ranojoy Bose, Tho Tran +3
We develop a thermally tunable hybrid photonic platform comprising gallium arsenide (GaAs) photonic crystal cavities, silicon nitride (SiN) grating couplers and waveguides, and…
Towards Trainable Media: Using Waves for Neural Network-Style Training
Michiel Hermans, Thomas Van Vaerenbergh
In this paper we study the concept of using the interaction between waves and a trainable medium in order to construct a matrix-vector multiplier. In particular we study such a dev…
Trainable and Dynamic Computing: Error Backpropagation through Physical Media
Michiel Hermans, Michaël Burm, Joni Dambre +1
Machine learning algorithms, and more in particular neural networks, arguably experience a revolution in terms of performance. Currently, the best systems we have for speech recogn…