Publications associées à Scale-out Systolic Arrays

Publications associées (15)

Differences in Visual Perception in Humans and Deep Neural Networks

Visual grouping is the ability to combine parts of objects to form whole objects, and it is crucial for general and robust vision. Despite this, a comprehensive model of how the process works in humans remains elusive. In my thesis, I investigate how well ...

EPFL2025

High-resolution predictions of the coolant properties for the 3D PWR core with artificial neural networks based on CTF

Marianna Papadionysiou

PSI and North Carolina State University are developing a high-resolution multi-physics core solver for Pressurized Water Reactor (PWR) analysis in Cartesian geometry, using the neutron transport code nTRACER and two Machine Learning (ML) models providing t ...

2025

Programming nonlinear propagation for efficient optical learning machines

Demetri Psaltis, Christophe Moser, Niyazi Ulas Dinç, Ugur Tegin, Ilker Oguz, Mustafa Yildirim, Carlo Gigli, Jih-Liang Hsieh

The ever-increasing demand for training and inferring with larger machine-learning models requires more efficient hardware solutions due to limitations such as power dissipation and scalability. Optics is a promising contender for providing lower power com ...

Spie-Soc Photo-Optical Instrumentation Engineers2024

Hierarchical Training of Deep Neural Networks Using Early Exiting

Pascal Frossard, Liza Carol Andrea Dunbar, Ahmet Caner Yüzügüler, Yamin Sepehri

Deep neural networks (DNNs) provide state-of-the-art accuracy for vision tasks, but they require significant resources for training. Thus, they are trained on cloud servers far from the edge devices that acquire the data. This issue increases communication ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs

Florent Gérard Krzakala, Bruno Loureiro, Ludovic Théo Stephan, Yatin Dandi, Luca Pesce, Luca Arnaboldi

We study the impact of the batch

n_b

on the iteration time

T

of training two-layer neural networks with one-pass stochastic gradient descent (SGD) on multi-index target functions of isotropic covariates. We characterize the optimal batch size minimizin ...

JMLR2024

Technology Mapping and Optimization Algorithms for Logic Synthesis of Advanced Technologies

Alessandro Tempia Calvino

Logic synthesis is a key component of electronic design automation (EDA) tools, essential for designing high-performance, compact, and power-efficient integrated circuits. The continuous downscaling of complementary metal-oxide-semiconductor (CMOS) technol ...

EPFL2024

∇2DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials

Egor Rumiantsev

Methods of computational quantum chemistry provide accurate approximations of molecular properties crucial for computer-aided drug discovery and other areas of chemical science. However, high computational complexity limits the scalability of their applica ...

Neural information processing systems foundation2024

Forward-forward training of an optical neural network

Demetri Psaltis, Christophe Moser, Niyazi Ulas Dinç, Ilker Oguz, Mustafa Yildirim, Jih-Liang Hsieh

Neural networks (NNs) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementations. Optics-based platforms, using technologies such as silicon photonics ...

Optica Publishing Group2023

The One-Way Communication Complexity of Submodular Maximization with Applications to Streaming and Robustness

Ola Nils Anders Svensson

We consider the classical problem of maximizing a monotone submodular function subject to a cardinality constraint, which, due to its numerous applications, has recently been studied in various computational models. We consider a clean multiplayer model th ...

ASSOC COMPUTING MACHINERY2023

High-speed identification of suspended carbon nanotubes using Raman spectroscopy and deep learning

Martin Jaggi, Luis Felipe Barba Flores

The identification of nanomaterials with the properties required for energy-efficient electronic systems is usually a tedious human task. A workflow to rapidly localize and characterize nanomaterials at the various stages of their integration into large-sc ...

SPRINGERNATURE2022

Chronic recording of cortical activit y underlying vocalization in awake minipigs

Stéphanie Lacour, Giuseppe Schiavone, Florian Dylan Fallegger

Background: Investigating brain dynamics underlying vocal production in animals is a powerf u l w a y to inform on the neural bases of human speech. In particular, brain networks underlying vocal production in non-human primates show striking similarities ...

ELSEVIER2022

Indirect Local Attacks for Context-aware Semantic Segmentation Networks

Mathieu Salzmann, Krishna Kanth Nakka

Recently, deep networks have achieved impressive semantic segmentation performance, in particular thanks to their use of larger contextual information. In this paper, we show that the resulting networks are sensitive not only to global adversarial attacks, ...

Springer Nature2020

Tail-tolerance as a Systems Principle not a Metric

Edouard Bugnion, Evangelos Marios Kogias

Tail-latency tolerance (or just simply tail-tolerance) is the ability for a system to deliver a response with low-latency nearly all the time. It it typically expressed as a system metric (e.g., the 99th or 99.99th percentile latency) or as a service-level ...

Assoc Computing Machinery2020

Capsule networks explain complex spatial processing

Michael Herzog, Adrien Christophe Doerig

Classically, visual processing is described as a cascade of local feedforward computations. This view has received striking support from the success of convolutional neural networks (CNNs). However, CNNs only roughly mimic human vision. For example, CNNs d ...

SAGE PUBLICATIONS LTD2019

Seamless Hardware-Software Integration in Reconfigurable Computing Systems

Paolo Ienne, Miljan Vuletic

2005