Related papers: Demystifying FPGA Hard NoC Performance

We propose an efficient distributed out-of-memory implementation of the Non-negative Matrix Factorization (NMF) algorithm for heterogeneous high-performance-computing (HPC) systems. The proposed implementation is based on prior work on…

Performance of distributed data center applications can be improved through use of FPGA-based SmartNICs, which provide additional functionality and enable higher bandwidth communication. Until lately, however, the lack of a simple approach…

This paper presents the evaluation of a Network-on-Chip (NoC) that offers load balancing for Systems-on-Chip (SoCs) dedicated for multimedia applications that require high traffic of variable bitrate communication. The NoC is based on a…

Convolutional Neural Networks (CNN) have been widely deployed in diverse application domains. There has been significant progress in accelerating both their training and inference using high-performance GPUs, FPGAs, and custom ASICs for…

Neuromorphic computing leveraging spiking neural network has emerged as a promising solution to tackle the security and reliability challenges with the conventional cyber-physical infrastructure of microgrids. Its event-driven paradigm…

The use of Field Programmable Gate Arrays (FPGAs) to accelerate computational kernels has the potential to be of great benefit to scientific codes and the HPC community in general. With the recent developments in FPGA programming…

FPGAs have shown great potential in providing low-latency and energy-efficient solutions for deep neural network (DNN) inference applications. Currently, the majority of FPGA-based DNN accelerators in the cloud run in a time-division…

We explore the use of FCNNs (Fully Connected Neural Networks) for designing end-to-end communication systems without taking any inspiration from existing classical communications models or error control coding. This work relies solely on…

Due to the irregular nature of connections in most graph datasets, partitioning graph analysis algorithms across multiple computational nodes that do not share a common memory inevitably leads to large amounts of interconnect traffic.…

To cope with the increasing demand and computational intensity of deep neural networks (DNNs), industry and academia have turned to accelerator technologies. In particular, FPGAs have been shown to provide a good balance between performance…

Neural Networks (NN) provide a solid and reliable way of executing different types of applications, ranging from speech recognition to medical diagnosis, speeding up onerous and long workloads. The challenges involved in their…

Edge-AI computing requires high energy efficiency, low power consumption, and relatively high flexibility and compact area, challenging the AI-chip design. This work presents a 0.96 pJ/SOP heterogeneous neuromorphic system-on-chip (SoC)…

Due to recent advances in digital technologies, and availability of credible data, an area of artificial intelligence, deep learning, has emerged, and has demonstrated its ability and effectiveness in solving complex learning problems not…

Embedded systems are parts of our daily life and used in many fields. They can be found in smartphones or in modern cars including GPS, light/rain sensors and other electronic assistance mechanisms. These systems may handle sensitive data…

In this paper, a weighted proportional fair (PF) scheduling method is proposed in the context of non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) at the receiver side. The new scheme introduces weights…

Noisy labels in distributed datasets induce severe local overfitting and consequently compromise the global model in federated learning (FL). Most existing solutions rely on selecting clean devices or aligning with public clean datasets,…

While FPGA accelerator boards and their respective high-level design tools are maturing, there is still a lack of multi-FPGA applications, libraries, and not least, benchmarks and reference implementations towards sustained HPC usage of…

Trends in hardware, the prevalence of the cloud, and the rise of highly demanding applications have ushered an era of specialization that quickly changes how data is processed at scale. These changes are likely to continue and accelerate in…

Modern large-scale computing systems (data centers, supercomputers, cloud and edge setups and high-end cyber-physical systems) employ heterogeneous architectures that consist of multicore CPUs, general-purpose many-core GPUs, and…

Deep Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in a wide range of applications. However, deeper CNN models, which are usually computation consuming, are widely required for complex Artificial…