19212

Posts

Dec, 1

Titan: A Parallel Asynchronous Library for Multi-Agent and Soft-Body Robotics using NVIDIA CUDA

While most robotics simulation libraries are built for low-dimensional and intrinsically serial tasks, soft-body and multi-agent robotics have created a demand for simulation environments that can model many interacting bodies in parallel. Despite the increasing interest in these fields, no existing simulation library addresses the challenge of providing a unified, highly-parallelized, GPU-accelerated interface for simulating […]
Dec, 1

FusionStitching: Boosting Execution Efficiency of Memory Intensive Computations for DL Workloads

Performance optimization is the art of continuous seeking a harmonious mapping between the application domain and hardware. Recent years have witnessed a surge of deep learning (DL) applications in industry. Conventional wisdom for optimizing such workloads mainly focus on compute intensive ops (GEMM, Convolution, etc). Yet we show in this work, that the performance of […]
Nov, 24

Understanding the Performance of HPC Applications

High performance computing is an important asset to scientific research, enabling the study of phenomena such as nuclear physics or climate change, that are difficult or impossible to be studied in traditional experiments or allowing researchers to utilize large amounts of data from experiments such as the Large Hadron Collider. No matter the use of […]
Nov, 24

Benchmarking Deep Learning Models on Jetson TX2

In conclusion, the present work brings an overview of artificial intelligence and, mainly, deep learning fields with a focus on image recognition and the history behind the models and techniques present nowadays. Beyond that, we explored how embedded hardware work with the new scenarios that AI brings to the table and how companies are developing […]
Nov, 24

Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU

There is growing interest in graph mining algorithms such as motif counting. Generic graph mining systems have been developed to provide unified interfaces for programming these algorithms. However, existing systems take minutes or even hours to mine even simple patterns in moderate-sized graphs, which significantly limits their real-world usability. We present Pangolin, a high-performance and […]
Nov, 24

FeCaffe: FPGA-enabled Caffe with OpenCL for Deep Learning Training and Inference on Intel Stratix 10

Deep learning and Convolutional Neural Network (CNN) have becoming increasingly more popular and important in both academic and industrial areas in recent years cause they are able to provide better accuracy and result in classification, detection and recognition areas, compared to traditional approaches. Currently, there are many popular frameworks in the market for deep learning […]
Nov, 24

Hacking Neural Networks: A Short Introduction

A large chunk of research on the security issues of neural networks is focused on adversarial attacks. However, there exists a vast sea of simpler attacks one can perform both against and with neural networks. In this article, we give a quick introduction on how deep learning in security works and explore the basic methods […]
Nov, 17

A Computing Kernel for Network Binarization on PyTorch

Deep Neural Networks have now achieved state-of-the-art results in a wide range of tasks including image classification, object detection and so on. However, they are both computation consuming and memory intensive, making them difficult to deploy on low-power devices. Network binarization is one of the existing effective techniques for model compression and acceleration, but there […]
Nov, 17

Compiler-Driven Performance on Heterogeneous Computing Platforms

Modern parallel programming languages such as OpenMP provide simple, portable programming models that support offloading of computation to various accelerator devices. Coupled with the increasing prevalence of heterogeneous computing platforms and the battle for supremacy in the co-processor space, gives rise to additional challenges placed on compiler/runtime vendors to handle the increasing complexity and diversity […]
Nov, 17

word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement

Deep learning natural language processing models often use vector word embeddings, such as word2vec or GloVe, to represent words. A discrete sequence of words can be much more easily integrated with downstream neural layers if it is represented as a sequence of continuous vectors. Also, semantic relationships between words, learned from a text corpus, can […]
Nov, 17

Deep Learning Based FPGA-CPU Acceleration

The purpose of this project is to continue exploring new ways of accelerating sequential computer code, and finding out if the machine learning techniques available today are able to help us in this task. The core idea is trying to parallelize during run-time (in a way completely transparent to the programmer) the code that’s being […]
Nov, 17

A Highly Parameterizable Framework for Conditional Restricted Boltzmann Machine Based Workloads Accelerated With FPGAs and OpenCL

Conditional Restricted Boltzmann Machine (CRBM) is a promising candidate for a multidimensional system modeling that can learn a probability distribution over a set of data. It is a specific type of an artificial neural network with one input (visible) and one output (hidden) layer. Recently published works demonstrate that CRBM is a suitable mechanism for […]

* * *

* * *

HGPU group © 2010-2020 hgpu.org

All rights belong to the respective authors

Contact us: