Related papers: A novel approach for FPGA-to-server data transmiss…
An ultra-high-speed waveform digitizer prototype based on gigabit Ethernet has been developed. The prototype is designed to read out signals of detectors to realize the accurate measurement of various physical quantities for plasma…
In this thesis we investigate high throughput computational methods for processing large quantities of data collected from synchrotrons and their application to spectral analysis of powder diffraction data. We also present the main product…
The User Datagram Protocol (UDP) and other similar protocols send the application data from the source machine to the destination machine inside segments, without foreseeing nor allowing for any type of control on the transmission or…
In edge inference, an edge server provides remote-inference services to edge devices. This requires the edge devices to upload high-dimensional features of data samples over resource-constrained wireless channels, which creates a…
Servers produced by mainstream vendors are inefficient in processing Big Data queries due to bottlenecks inherent in the fundamental architecture of these systems. Current server blades contain multicore processors connected to DRAM memory…
Adversarial attacks in black-box settings are highly practical, with transfer-based attacks being the most effective at generating adversarial examples (AEs) that transfer from surrogate models to unseen target models. However, their…
We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on FPGAs. By extending the hls4ml library, we demonstrate an inference latency of $5\,\mu$s using convolutional…
Convolutional Neural Networks (CNNs) combine large amounts of parallelizable computation with frequent memory access. Field Programmable Gate Arrays (FPGAs) can achieve low latency and high throughput CNN inference by implementing dataflow…
In this paper we present a novel high-resolution multi-channel FPGA-based time-to-digital converter (TDC). We designed and implemented a complex electronic circuit on the FPGA, whose overall accuracy is several orders of magnitude greater…
It is commonly assumed that the end-to-end networking performance of edge offloading is purely dictated by that of the network connectivity between end devices and edge computing facilities, where ongoing innovation in 5G/6G networking can…
The determination of charged particle trajectories (tracking) in collisions at the CERN Large Hadron Collider (LHC) is one of the most important aspects for event reconstruction at hadron colliders. This is especially true in the high…
Computationally expensive, high-accuracy detector simulations are a major bottleneck for many particle physics experiments such as those at the Large Hadron Collider (LHC) as well as those planned for future colliders. This challenge has…
Efficient implementations of parallel applications on heterogeneous hybrid architectures require a careful balance between computations and communications with accelerator devices. Even if most of the communication time can be overlapped by…
Modern data analytics requires a huge amount of computing power and processes a massive amount of data. At the same time, the underlying computing platform is becoming much more heterogeneous on both hardware and software. Even though…
Even though it seems that FPGAs have finally made the transition from research labs to the consumer devices' market, programming them remains challenging. Despite the improvements made by High-Level Synthesis (HLS), which removed the…
Convolutional Neural Networks (CNNs) are widely used in deep learning applications, e.g. visual systems, robotics etc. However, existing software solutions are not efficient. Therefore, many hardware accelerators have been proposed…
Accelerating Human Action Recognition (HAR) efficiently for real-time surveillance and robotic systems on edge chips remains a challenging research field, given its high computational and memory requirements. This paper proposed an…
The challenges involved in executing neural networks (NNs) at the edge include providing diversity, flexibility, and sustainability. That implies, for instance, supporting evolving applications and algorithms energy-efficiently. Using…
FPGAs are well-suited for dataflow architectures that process data in a streaming or pipelined manner, thus satisfying the high computational and communication demands of emerging applications. However, manually implementing an efficient…
Most traditional false data injection attack (FDIA) detection approaches rely on a key assumption, i.e., the power system can be accurately modeled. However, the transmission line parameters are dynamic and cannot be accurately known during…