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Posts

Feb, 2

Noise Removal from Remote Sensed Images by NonLocal Means with OpenCL Algorithm

We introduce a multi-platform portable implementation of the NonLocal Means methodology aimed at noise removal from remotely sensed images. It is particularly suited for hyperspectral sensors for which real-time applications are not possible with only CPU based algorithms. In the last decades computational devices have usually been a compound of cross-vendor sets of specifications (heterogeneous […]
Feb, 2

Interoperable GPU Kernels as Latency Improver for MEC

Mixed reality (MR) applications are expected to become common when 5G goes mainstream. However, the latency requirements are challenging to meet due to the resources required by video-based remoting of graphics, that is, decoding video codecs. We propose an approach towards tackling this challenge: a client-server implementation for transacting intermediate representation (IR) between a mobile […]
Jan, 26

Using Parallel Programming Models for Automotive Workloads on Heterogeneous Systems – a Case Study

Due to the ever-increasing computational demand of automotive applications, and in particular autonomous driving functionalities, the automotive industry and supply vendors are starting to adopt parallel and heterogeneous embedded platforms for their products. However, C and C++, the currently dominating programming languages in this industry, do not provide sufficient mechanisms to target such platforms. Established […]
Jan, 26

Hardware/Software Co-Design for Data-Intensive Genomics Workloads

Since the last decade, the main components of computer systems have been evolving, diversifying, to overcome their physical limits and to minimize their energy footprint. Hardware specialization and heterogeneity have become key to design more efficient systems and tackle ever-important problems with ever-larger volumes of data. However, to fully take advantage of the new hardware, […]
Jan, 26

Automatically Harnessing Sparse Acceleration

Sparse linear algebra is central to many scientific programs, yet compilers fail to optimize it well. High-performance libraries are available, but adoption costs are significant. Moreover, libraries tie programs into vendor-specific software and hardware ecosystems, creating non-portable code. In this paper, we develop a new approach based on our specification Language for implementers of Linear […]
Jan, 26

Efficient Radial Pattern Keyword Search on Knowledge Graphs in Parallel

Recently, keyword search on Knowledge Graphs (KGs) becomes popular. Typical keyword search approaches aim at finding a concise subgraph from a KG, which can reflect a close relationship among all input keywords. The connection paths between keywords are selected in a way that leads to a result subgraph with a better semantic score. However, such […]
Jan, 26

An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

Weight pruning has been widely acknowledged as a straightforward and effective method to eliminate redundancy in Deep Neural Networks (DNN), thereby achieving acceleration on various platforms. However, most of the pruning techniques are essentially trade-offs between model accuracy and regularity which lead to impaired inference accuracy and limited on-device acceleration performance. To solve the problem, […]
Jan, 19

md_poly: A Performance-Portable Polyhedral Compiler Based on Multi-Dimensional Homomorphisms

Polyhedral compilers automatically parallelize sequential programs for multi- and many-core architectures, such as CPU and GPU. However, parallel code generated by state-ofthe-art polyhedral compilers often lacks performance portability, because the existing compilers are usually optimized toward only a single particular architecture (e.g., GPU). Moreover, even on their target architecture, polyhedral compilers sometimes fail to reach […]
Jan, 19

On Demand Solid Texture Synthesis Using Deep 3D Networks

This paper describes a novel approach for on demand volumetric texture synthesis based on a deep learning framework that allows for the generation of high quality 3D data at interactive rates. Based on a few example images of textures, a generative network is trained to synthesize coherent portions of solid textures of arbitrary sizes that […]
Jan, 19

GPU Tensor Cores for fast Arithmetic Reductions

This work proposes a GPU tensor core approach that encodes the arithmetic reduction of n numbers as a set of chained mxm matrix multiply accumulate (MMA) operations executed in parallel by GPU tensor cores. The asymptotic running time of the proposed chained tensor core approach is T(n)=5 log_m^2 n and its speedup is S=4/5 log_2 […]
Jan, 19

Hardware Implementation and Quantization of Tiny-Yolo-v2 using OpenCL

The trend of increasingly model size in Deep Neural Network (DNN) algorithms boost the performance of visual recognition tasks. These gains in performance have come at a cost of increase in computational complexity and memory bandwidth. Recent studies have explored the fixed-point implementation of DNN algorithms such as AlexNet and VGG on Field Programmable Gate […]
Jan, 19

Towards High Performance Java-based Deep Learning Frameworks

The advent of modern cloud services along with the huge volume of data produced on a daily basis, have set the demand for fast and efficient data processing. This demand is common among numerous application domains, such as deep learning, data mining, and computer vision. Prior research has focused on employing hardware accelerators as a […]

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