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May, 12

Improving Resource Efficiency in Virtualized Datacenters

In recent years there has been an extraordinary growth of the Internet of Things (IoT) and its protocols. The increasing diffusion of electronic devices with identification, computing and communication capabilities is laying ground for the emergence of a highly distributed service and networking environment. The above mentioned situation implies that there is an increasing demand […]
May, 12

FPGA Implementation of Reduced Precision Convolutional Neural Networks

With the improvement in processing systems, machine learning applications are finding widespread use in almost all sectors of technology. Image recognition is one application of machine learning which has become widely popular with various architectures and systems aimed at improving recognition performance. With classification accuracy now approaching saturation point, many researchers are now focusing on […]
May, 12

Arbitrarily large iterative tomographic reconstruction on multiple GPUs using the TIGRE toolbox

Tomographic image sizes keep increasing over time and while the GPUs that compute the tomographic reconstruction are also increasing in memory size, they are not doing so fast enough to reconstruct the largest datasets. This problem is often solved by reconstructing data in large clusters of GPUs with enough devices to fit the measured X-ray […]
May, 12

Predictable GPGPU Computing in DNN-Driven Autonomous Systems

Graphics processing units (GPUs) are being widely used as co-processors in many domains to accelerate general-purpose workloads that are data-parallel and computationally intensive, i.e., GPGPU. An emerging usage domain is adopting GPGPU to accelerate inherently computation-intensive Deep Neural Network (DNN) workloads in autonomous systems. Such autonomous systems are usually time-sensitive, especially for autonomous driving systems. […]
May, 12

Performance Engineering for a Tall & Skinny Matrix Multiplication Kernel on GPUs

General matrix-matrix multiplications (GEMM) in vendor-supplied BLAS libraries are best optimized for square matrices but often show bad performance for tall & skinny matrices, which are much taller than wide. Nvidia’s current CUBLAS implementation delivers only a fraction of the potential performance (as given by the roofline model) in this case. We describe the challenges […]
May, 8

FPGA-based acceleration of a particle simulation High Performance Computing application

In the present thesis, it has been studied the possibility to insert FPGAs in the world of High Performance Computing (HPC) systems. Such systems are hybrid platforms that exploit the pure parallel computation of GPUs in order to reach very high performances. Nevertheless, GPU-based systems are power-hungry and require a power consumption so large, that […]
May, 8

Charactering and Detecting CUDA Program Bugs

While CUDA has become a major parallel computing platform and programming model for general-purpose GPU computing, CUDA-induced bug patterns have not yet been well explored. In this paper, we conduct the first empirical study to reveal important categories of CUDA program bug patterns based on 319 bugs identified within 5 popular CUDA projects in GitHub. […]
May, 8

TensorNetwork: A Library for Physics and Machine Learning

TensorNetwork is an open source library for implementing tensor network algorithms. Tensor networks are sparse data structures originally designed for simulating quantum many-body physics, but are currently also applied in a number of other research areas, including machine learning. We demonstrate the use of the API with applications both physics and machine learning, with details […]
May, 5

Principles, Techniques, and Tools for Explicit and Automatic Parallelization

The end of Dennard scaling also brought an end to frequency scaling as a means to improve performance. Chip manufacturers had to abandon frequency and superscalar scaling as processors became increasingly power constrained. An architecture’s power budget became the limiting factor to performance gains, and computations had to be performed more energy-efficiently. Designers turned to […]
May, 5

Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems

Deep neural networks (DNNs) have been quite successful in solving many complex learning problems. However, DNNs tend to have a large number of learning parameters, leading to a large memory and computation requirement. In this paper, we propose a model compression framework for efficient training and inference of deep neural networks on embedded systems. Our […]
May, 5

An Architectural Journey into RISC Architectures for HPC Workloads

The race to the Exascale (i.e., 10^18 Floating Point operations per seconds) together with the slow-down of Moore’s law are posing unprecedented challenges to the whole High-Performance Computing (HPC) community. Computer architects, system integrators and software engineers studying programming models for handling parallelism are especially called to the rescue in a moment like the one […]
May, 5

Full-stack Optimization for Accelerating CNNs with FPGA Validation

We present a full-stack optimization framework for accelerating inference of CNNs (Convolutional Neural Networks) and validate the approach with field-programmable gate arrays (FPGA) implementations. By jointly optimizing CNN models, computing architectures, and hardware implementations, our full-stack approach achieves unprecedented performance in the trade-off space characterized by inference latency, energy efficiency, hardware utilization and inference accuracy. […]

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