Related papers: VeBPF Many-Core Architecture for Network Functions…

In this paper, the foundations of neuromorphic computing, spiking neural networks (SNNs) and memristors, are analyzed and discussed. Neuromorphic computing is then applied to FPGA design for digital signal processing (DSP). Finite impulse…

In this report we present a network-level multi-core energy model and a software development process workflow that allows software developers to estimate the energy consumption of multi-core embedded programs. This work focuses on a high…

With the emerging big data applications of Machine Learning, Speech Recognition, Artificial Intelligence, and DNA Sequencing in recent years, computer architecture research communities are facing the explosive scale of various data…

We carry out a comparative performance study of multi-core CPUs, GPUs and Intel Xeon Phi (Many Integrated Core - MIC) with a microscopy image analysis application. We experimentally evaluate the performance of computing devices on core…

Recent advances in computing architectures and networking are bringing parallel computing systems to the masses so increasing the number of potential users of these kinds of systems. In particular, two important technological evolutions are…

Since the introduction of the first Bitcoin blockchain in 2008, different decentralized blockchain systems such as Ethereum, Hyperledger Fabric, and Corda, have emerged with public and private accessibility. It has been widely acknowledged…

FPGAs are rarely mentioned when discussing the implementation of large machine learning applications, such as Large Language Models (LLMs), in the data center. There has been much evidence showing that single FPGAs can be competitive with…

The emergence of heterogeneity and domain-specific architectures targeting deep learning inference show great potential for enabling the deployment of modern CNNs on resource-constrained embedded platforms. A significant development is the…

The diversity of Fog Computing deployment models and the lack of publicly available Fog infrastructure makes the design of an efficient application or resource management policy a challenging task. Such research often requires a test…

In recent years the computing landscape has seen an in- creasing shift towards specialized accelerators. Field pro- grammable gate arrays (FPGAs) are particularly promising as they offer significant performance and energy improvements…

Edge AI applications increasingly require models that can learn and adapt on-device with minimal energy budget. Traditional deep learning models, while powerful, are often overparameterized, energy-hungry, and dependent on cloud…

Recently, Python Testbed for Federated Learning Algorithms emerged as a low code and generative large language models amenable framework for developing decentralized and distributed applications, primarily targeting edge systems, by…

This paper presents a novel FPGA architecture for implementing various styles of asynchronous logic. The main objective is to break the dependency between the FPGA architecture dedicated to asynchronous logic and the logic style. The…

Heterogeneous embedded systems on chip (HESoCs) co-integrate a standard host processor with programmable manycore accelerators (PMCAs) to combine general-purpose computing with domain-specific, efficient processing capabilities. While…

For an increasing number of data intensive scientific applications, parallel I/O concepts are a major performance issue. Tackling this issue, we develop an input/output system designed for highly efficient, scalable and conveniently usable…

Reliability and real-time responsiveness in safety-critical systems have traditionally been achieved using error detection mechanisms, such as LockStep, which require pre-configured checker cores,strict synchronisation between main and…

To cope with the soft errors and make full use of the multi-core system, this paper gives an efficient fault-tolerant hardware and software co-designed architecture for multi-core systems. And with a not large number of test patterns, it…

Though deep neural network models exhibit outstanding performance for various applications, their large model size and extensive floating-point operations render deployment on mobile computing platforms a major challenge, and, in…

This article describes high-speed multiprocessor architecture for the concurrent analyzing information represented in analytic, graph- and table forms of associative relations to search, recognize and make a decision in n-dimensional vector…

The extended Berkeley Packet Filter (eBPF) is extensively utilized for observability and performance analysis in cloud-native environments. However, deploying eBPF programs across a heterogeneous cloud environment presents challenges,…