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Integrated electro-optic attention nonlinearities for transformers
Authors:
Luis Mickeler,
Kai Lion,
Alfonso Nardi,
Jost Kellner,
Pierre Didier,
Bhavin J. Shastri,
Niao He,
Rachel Grange
Abstract:
Transformers have emerged as the dominant neural-network architecture, achieving state-of-the-art performance in language processing and computer vision. At the core of these models lies the attention mechanism, which requires a nonlinear, non-negative mapping using the Softmax function. However, although Softmax operations account for less than 1% of the total operation count, they can disproport…
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Transformers have emerged as the dominant neural-network architecture, achieving state-of-the-art performance in language processing and computer vision. At the core of these models lies the attention mechanism, which requires a nonlinear, non-negative mapping using the Softmax function. However, although Softmax operations account for less than 1% of the total operation count, they can disproportionately bottleneck overall inference latency. Here, we use thin-film lithium niobate (TFLN) Mach-Zehnder modulators (MZMs) as analog nonlinear computational elements to drastically reduce the latency of nonlinear computations. We implement electro-optic alternatives to digital Softmax and Sigmoid, and evaluate their performance in Vision Transformers and Large Language Models. Our system maintains highly competitive accuracy, even under aggressive 4-bit input-output quantization of the analog units. We further characterize system noise at encoding speeds up to 10 GBaud and assess model robustness under various noise conditions. Our findings suggest that TFLN modulators can serve as nonlinear function units within hybrid co-packaged hardware, enabling high-speed and energy-efficient nonlinear computation.
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Submitted 10 April, 2026;
originally announced April 2026.
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Optical Computing with Spectrally Multiplexed Features in Complex Media
Authors:
Xue Dong,
Kai Lion,
Fei Xia,
YoonSeok Baek,
Ziao Wang,
Niao He,
Sylvain Gigan
Abstract:
Artificial intelligence (AI) has rapidly evolved into a critical technology; however, electrical hardware struggles to keep pace with the exponential growth of AI models. Free space optical hardware provides alternative approaches for large-scale optical processing, and in-memory computing, with applications across diverse machine learning tasks. Here, we explore the use of broadband light scatter…
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Artificial intelligence (AI) has rapidly evolved into a critical technology; however, electrical hardware struggles to keep pace with the exponential growth of AI models. Free space optical hardware provides alternative approaches for large-scale optical processing, and in-memory computing, with applications across diverse machine learning tasks. Here, we explore the use of broadband light scattering in free-space optical components, specifically complex media, which generate uncorrelated optical features at each wavelength. By treating individual wavelengths as independent predictors, we demonstrate improved classification accuracy through in-silico majority voting, along with the ability to estimate uncertainty without requiring access to the model's probability outputs. We further demonstrate that linearly combining multiwavelength features, akin to spectral shaping, enables us to tune output features with improved performance on classification tasks, potentially eliminating the need for multiple digital post-processing steps. These findings illustrate the spectral multiplexing or broadband advantage for free-space optical computing.
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Submitted 5 October, 2025;
originally announced October 2025.
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Roadmap on Advancements of the FHI-aims Software Package
Authors:
Joseph W. Abbott,
Carlos Mera Acosta,
Alaa Akkoush,
Alberto Ambrosetti,
Viktor Atalla,
Alexej Bagrets,
Jörg Behler,
Daniel Berger,
Hannah Bertschi,
Björn Bieniek,
Jonas Björk,
Volker Blum,
Saeed Bohloul,
Connor L. Box,
Nicholas Boyer,
Danilo Simoes Brambila,
Gabriel A. Bramley,
Kyle R. Bryenton,
María Camarasa-Gómez,
Christian Carbogno,
Fabio Caruso,
Sucismita Chutia,
Michele Ceriotti,
Gábor Csányi,
William Dawson
, et al. (181 additional authors not shown)
Abstract:
Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accuracy at the base, reliable predictions are unlikely at any level that follows. The software package FHI-aims has proven to be a game changer for accurate free-energy calculations because of its scalability, numerical precis…
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Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accuracy at the base, reliable predictions are unlikely at any level that follows. The software package FHI-aims has proven to be a game changer for accurate free-energy calculations because of its scalability, numerical precision, and its efficient handling of density functional theory (DFT) with hybrid functionals and van der Waals interactions. It treats molecules, clusters, and extended systems (solids and liquids) on an equal footing. Besides DFT, FHI-aims also includes quantum-chemistry methods, descriptions for excited states and vibrations, and calculations of various types of transport. Recent advancements address the integration of FHI-aims into an increasing number of workflows and various artificial intelligence (AI) methods. This Roadmap describes the state-of-the-art of FHI-aims and advancements that are currently ongoing or planned.
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Submitted 20 April, 2026; v1 submitted 30 April, 2025;
originally announced May 2025.
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Solvers for Large-Scale Electronic Structure Theory: ELPA and ELSI
Authors:
Petr Karpov,
Andreas Marek,
Tobias Melson,
Alexander Pöppl,
Victor Wen-zhe Yu,
Ben Hourahine,
Alberto Garcia,
William Dawson,
Yi Yao,
William Huhn,
Jonathan Moussa,
Sam Hall,
Reinhard Maurer,
Uthpala Herath,
Konstantin Lion,
Sebastian Kokott,
Volker Blum
Abstract:
In this contribution, we give an overview of the ELPA library and ELSI interface, which are crucial elements for large-scale electronic structure calculations in FHI-aims.
ELPA is a key solver library that provides efficient solutions for both standard and generalized eigenproblems, which are central to the Kohn-Sham formalism in density functional theory (DFT). It supports CPU and GPU architect…
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In this contribution, we give an overview of the ELPA library and ELSI interface, which are crucial elements for large-scale electronic structure calculations in FHI-aims.
ELPA is a key solver library that provides efficient solutions for both standard and generalized eigenproblems, which are central to the Kohn-Sham formalism in density functional theory (DFT). It supports CPU and GPU architectures, with full support for NVIDIA and AMD GPUs, and ongoing development for Intel GPUs. Here we also report the results of recent optimizations, leading to significant improvements in GPU performance for the generalized eigenproblem.
ELSI is an open-source software interface layer that creates a well-defined connection between "user" electronic structure codes and "solver" libraries for the Kohn-Sham problem, abstracting the step between Hamilton and overlap matrices (as input to ELSI and the respective solvers) and eigenvalues and eigenvectors or density matrix solutions (as output to be passed back to the "user" electronic structure code). In addition to ELPA, ELSI supports solvers including LAPACK and MAGMA, the PEXSI and NTPoly libraries (which bypass an explicit eigenvalue solution), and several others.
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Submitted 4 February, 2025;
originally announced February 2025.