Related papers: ELT-scale Adaptive Optics real-time control with t…

Non-common path quasi-static and differential aberrations are one of the big hurdles of direct imaging for current and future high-contrast imaging instruments. They increase speckle and photon noise thus reducing the achievable contrast…

Deep learning is an important component of big-data analytic tools and intelligent applications, such as, self-driving cars, computer vision, speech recognition, or precision medicine. However, the training process is computationally…

The adoption of intelligent systems with Artificial Neural Networks (ANNs) embedded in hardware for real-time applications currently faces a growing demand in fields like the Internet of Things (IoT) and Machine to Machine (M2M). However,…

This paper concerns development of a high-performance implementation of the Particle-in-Cell method for plasma simulation on Intel Xeon Phi coprocessors. We discuss suitability of the method for Xeon Phi architecture and present our…

Direct detection and detailed characterization of exoplanets using extreme adaptive optics (ExAO) is a key science goal of future extremely large telescopes (ELTs). However, wavefront errors will limit the sensitivity of this endeavor.…

A new generation of radio telescopes is achieving unprecedented levels of sensitivity and resolution, as well as increased agility and field-of-view, by employing high-performance digital signal processing hardware to phase and correlate…

Traditional wavefront control in high-energy, high-intensity laser systems usually lacks real-time capability, failing to address dynamic aberrations. This limits experimental accuracy due to shot-to-shot fluctuations and necessitates long…

Free-space optical communications with moving targets, such as satellite terminals, demand ultrafast wavefront sensing and correction. This is typically addressed using a Shack-Hartmann sensor, which pairs a high-speed camera with a lenslet…

Recently, foundation models based on Vision Transformers (ViTs) have become widely available. However, their fine-tuning process is highly resource-intensive, and it hinders their adoption in several edge or low-energy applications. To this…

The discrete wavelet transform can be found at the heart of many image-processing algorithms. Until now, the transform on general-purpose processors (CPUs) was mostly computed using a separable lifting scheme. As the lifting scheme consists…

The direct imaging of potentially habitable Exoplanets is one prime science case for the next generation of high contrast imaging instruments on ground-based extremely large telescopes. To reach this demanding science goal, the instruments…

Recently, large-scale transformer-based models have been proven to be effective over various tasks across many domains. Nevertheless, applying them in industrial production requires tedious and heavy works to reduce inference costs. To fill…

Despite the rapid advancement of object detection algorithms, processing high-resolution images on embedded devices remains a significant challenge. Theoretically, the fully convolutional network architecture used in current real-time…

The escalating data volume and complexity resulting from the rapid expansion of artificial intelligence (AI), internet of things (IoT) and 5G/6G mobile networks is creating an urgent need for energy-efficient, scalable computing hardware.…

The explosive growth of Large Language Models (LLMs), such as GPT-4 with 1.8 trillion parameters, demands a fundamental rethinking of data center architecture to ensure scalability, efficiency, and cost-effectiveness. Our work provides a…

Computing $k$-Nearest Neighbors (KNN) is one of the core kernels used in many machine learning, data mining and scientific computing applications. Although kd-tree based $O(\log n)$ algorithms have been proposed for computing KNN, due to…

Electrical power requirements will be a constraint on the future growth of Distributed High Throughput Computing (DHTC) as used by High Energy Physics. Performance-per-watt is a critical metric for the evaluation of computer architectures…

The Cassiop\'ee project aims to develop the key technologies that will be used to deploy very high-performance Adaptive Optics for future ELTs. The ultimate challenge is to detect earth-like planets and characterize the composition of their…

The EAGLE instrument for the E-ELT is a multi-IFU spectrograph, that uses a MOAO system for wavefront correction of interesting lines of sight. We present a Monte-Carlo AO simulation package that has been used to model the performace of…

Integrated photonic convolution processors make optical neural networks (ONNs) a transformative solution for artificial intelligence applications such as machine vision. To enhance the parallelism, throughput, and energy efficiency of ONNs,…