Related papers: AURORA: A High Performance DAQ Framework for Next-…
Using artificial intelligent (AI) to re-design and enhance the current wireless communication system is a promising pathway for the future sixth-generation (6G) wireless network. The performance of AI-enabled wireless communication depends…
Multi-device inference can reduce Transformer latency by parallelizing computation. However, existing methods require high inter-device bandwidth, making them impractical for bandwidth-constrained environments. We present ASTRA, a…
It is a challenging task to design a random access protocol that achieves the optimal throughput in multi-cell random access with decentralized transmission due to the difficulty of coordination. In this paper, we present a decentralized…
Diffusion Transformers (DiTs) have emerged as the state-of-the-art backbone for high-fidelity image and video generation. However, their massive computational cost and memory footprint hinder deployment on edge devices. While post-training…
Policy optimization seeks the best solution to a control problem according to an objective or fitness function, serving as a fundamental field of engineering and research with applications in robotics. Traditional optimization methods like…
The growing demand for real-time DNN applications on edge devices necessitates faster inference of increasingly complex models. Although many devices include specialized accelerators (e.g., mobile GPUs), dynamic control-flow operators and…
The ngdp framework is intended to provide a base for the data acquisition (DAQ) system software. The ngdp's design key features are: high modularity and scalability; usage of the kernel context (particularly kernel threads) of the operating…
Recently, a quantum algorithm for a fundamentally important task in data mining, association rules mining (ARM), called qARM for short, has been proposed. Notably, qARM achieves significant speedup over its classical counterpart for…
The Askaryan Radio Array (ARA) experiment aims to detect ultra-high-energy cosmic neutrinos (>10 PeV) using radio detection techniques. To enhance ARA's capabilities, a new RFSoC-based DAQ, ARA-Next, is in the early stages of development.…
This paper presents Rudra, a parameter server based distributed computing framework tuned for training large-scale deep neural networks. Using variants of the asynchronous stochastic gradient descent algorithm we study the impact of…
The vision of AI-RAN convergence, as advocated by the AI-RAN Alliance, aims to unlock a unified 6G platform capable of seamlessly supporting AI and RAN workloads over shared infrastructure. However, the architectural framework and…
Autonomic computing has been proposed recently as a way to address the difficult management of applications whose complexity is constantly increasing. Autonomous applications will have to be especially flexible and be able to monitor…
We present T-DAQ-P, a compact and portable data acquisition and telemetry platform designed to support detector deployments in laboratory and field conditions by integrating event streaming, slow-control telemetry, and operator-oriented…
Cross-domain generalization is very important in Time Series Forecasting because similar historical information may lead to distinct future trends due to the domain-specific characteristics. Recent works focus on building unimodal time…
We introduce a new, open-source, Python module for the acquisition and processing of archival data from many X-ray telescopes - Democratising Archival X-ray Astronomy (hereafter referred to as DAXA). Our software is built to increase access…
Extremely large-scale antenna array (ELAA) technologies consisting of ultra-massive multiple-input-multiple-output (UM-MIMO) or reconfigurable intelligent surfaces (RISs), are emerging to meet the demand of wireless systems in…
We present QARMA, an efficient novel parallel algorithm for mining all Quantitative Association Rules in large multidimensional datasets where items are required to have at least a single common attribute to be specified in the rules single…
Deep research agents, which synthesize information across diverse sources, are significantly constrained by the sequential nature of reasoning. This bottleneck results in high latency, poor runtime adaptability, and inefficient resource…
The computing systems used by LHC experiments has historically consisted of the federation of hundreds to thousands of distributed resources, ranging from small to mid-size resource. In spite of the impressive scale of the existing…
Data assimilation (DA) is a fundamental component of modern weather prediction, yet it remains a major computational bottleneck in machine learning (ML)-based forecasting pipelines due to reliance on traditional variational methods. Recent…