Related papers: Demonstration of a context-switch method for heter…
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…
Today, there is a trend to incorporate more intelligence (e.g., vision capabilities) into a wide range of devices, which makes high performance a necessity for computing systems. Furthermore, for embedded systems, low power consumption…
Recent advancements in AI and edge computing have accelerated the development of machine-centric applications (MCAs), such as smart surveillance systems. In these applications, video cameras and sensors offload inference tasks like license…
Convolutional Neural Networks (CNNs) have a major impact on our society because of the numerous services they provide. On the other hand, they require considerable computing power. To satisfy these requirements, it is possible to use…
For the last three decades a core use of FPGAs has been for processing communication: FPGA-based SmartNICs are in widespread use from the datacenter to IoT. Augmenting switches with FPGAs, however, has been less studied, but has numerous…
There is a large body of legacy scientific code written in languages like Fortran that is not optimised to get the best performance out of heterogeneous acceleration devices like GPUs and FPGAs, and manually porting such code into parallel…
Developing high-performance and energy-efficient algorithms for maximum matchings is becoming increasingly important in social network analysis, computational sciences, scheduling, and others. In this work, we propose the first maximum…
Data sketches are a set of widely used approximated data summarizing techniques. Their fundamental property is sub-linear memory complexity on the input cardinality, an important aspect when processing streams or data sets with a vast base…
Modern multicore systems are migrating from homogeneous systems to heterogeneous systems with accelerator-based computing in order to overcome the barriers of performance and power walls. In this trend, FPGA-based accelerators are becoming…
The emergence of P4, a domain specific language, coupled to PISA, a domain specific architecture, is revolutionizing the networking field. P4 allows to describe how packets are processed by a programmable data plane, spanning ASICs and…
Field Programmable Gate Arrays (FPGAs) are more prone to be affected by transient faults in presence of radiation and other environmental hazards compared to Application Specific Integrated Circuits (ASICs). Hence, error mitigation and…
To accomplish the punctuation restoration task, most existing approaches focused on leveraging extra information (e.g., part-of-speech tags) or addressing the class imbalance problem. Recent works have widely applied the transformer-based…
A variety of computing platform like Field Programmable Gate Array (FPGA), Graphics Processing Unit (GPU) and multicore Central Processing Unit (CPU) in data centers are suitable for acceleration of data-intensive workloads. Especially,…
FPGA is appropriate for fix-point neural networks computing due to high power efficiency and configurability. However, its design must be intensively refined to achieve high performance using limited hardware resources. We present an…
In a dynamic heterogeneous environment, such as pervasive and ubiquitous computing, context-aware adaptation is a key concept to meet the varying requirements of different users. Connectivity is an important context source that can be…
In the trend towards hardware specialization, FPGAs play a dual role as accelerators for offloading, e.g., network virtualization, and as a vehicle for prototyping and exploring hardware designs. While FPGAs offer versatility and…
Heterogeneous embedded systems, with diverse computing elements and accelerators such as FPGAs, offer a promising platform for fast and flexible ML inference, which is crucial for services such as autonomous driving and augmented reality,…
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…
The rapid progress and advancement in electronic chips technology provide a variety of new implementation options for system engineers. The choice varies between the flexible programs running on a general-purpose processor (GPP) and the…
The advent of multipoint (multicast-based) applications and the growth and complexity of the Internet has complicated network protocol design and evaluation. In this paper, we present a method for automatic synthesis of worst and best case…