相关论文: Advances in Run-Time Performance and Interoperabil…
Modern commodity computing systems are composed by a number of different heterogeneous processing units, each of which has its own unique performance and energy characteristics. However, the majority of current network packet processing…
Neural Networks (NNs) have been widely adopted due to their outstanding efficacy and adaptability across computer vision and deep learning applications. The optimization of NNs is necessary to enable their deployment on energy constrained…
The rapid development in computing technology has paved the way for directive-based programming models towards a principal role in maintaining software portability of performance-critical applications. Efforts on such models involve a least…
Energy-aware architectures provide applications with a mix of low (LITTLE) and high (big) frequency cores. Choosing the best hardware configuration for a program running on such an architecture is difficult, because program parts benefit…
Hardware acceleration can revolutionize robotics, enabling new applications by speeding up robot response times while remaining power-efficient. However, the diversity of acceleration options makes it difficult for roboticists to easily…
Today the LHC offline computing relies heavily on CPU resources, despite the interest in compute accelerators, such as GPUs, for the longer term future. The number of cores per CPU socket has continued to increase steadily, reaching the…
Gaussian processes are widely used in machine learning domains but remain computationally demanding, limiting their efficient scalability across emerging hardware platforms. The GPRat library addresses these challenges using the HPX…
Virtualization is a key technology used in a wide range of applications, from cloud computing to embedded systems. Over the last few years, mainstream computer architectures were extended with hardware virtualization support, giving rise to…
The rapid growth of multi-core systems highlights the need for efficient Network-on-Chip (NoC) design to ensure seamless communication. Cache coherence, essential for data consistency, substantially reduces task computation time by enabling…
Chip industry continues advancing and expanding modern computing systems, resulting in more complex multi-core processors. Conversely, academic projects face scalability challenges due to limited resources, highlighting the need for…
FPGA accelerators on the NIC enable the offloading of expensive packet processing tasks from the CPU. However, FPGAs have limited resources that may need to be shared among diverse applications, and programming them is difficult. We present…
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…
For several years, MPI has been the de facto standard for writing parallel applications. One of the most popular MPI implementations is MPICH. Its successor, MPICH2, features a completely new design that provides more performance and…
The limited energy available in most embedded systems poses a significant challenge in enhancing the performance of embedded processors and microcontrollers. One promising approach to address this challenge is the use of approximate…
The capacity of offloading data and control tasks to the network is becoming increasingly important, especially if we consider the faster growth of network speed when compared to CPU frequencies. In-network compute alleviates the host CPU…
Recent advancements in quantization and mixed-precision approaches offers substantial opportunities to improve the speed and energy efficiency of Neural Networks (NN). Research has shown that individual parameters with varying low…
Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This work presents the software aspects of this endeavor for the…
Near-Data-Processing (NDP) architectures present a promising way to alleviate data movement costs and can provide significant performance and energy benefits to parallel applications. Typically, NDP architectures support several NDP units,…
CPU-FPGA heterogeneous architectures are attracting ever-increasing attention in an attempt to advance computational capabilities and energy efficiency in today's datacenters. These architectures provide programmers with the ability to…
This paper presents SynapticCore-X, a modular and resource-efficient neural processing architecture optimized for deployment on low-cost FPGA platforms. The design integrates a lightweight RV32IMC RISC-V control core with a configurable…