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Posts

Apr, 12

MNN: A Universal and Efficient Inference Engine

Deploying deep learning models on mobile devices draws more and more attention recently. However, designing an efficient inference engine on devices is under the great challenges of model compatibility, device diversity, and resource limitation. To deal with these challenges, we propose Mobile Neural Network (MNN), a universal and efficient inference engine tailored to mobile applications. […]
Apr, 12

Open Source Face Recognition API

Face recognition applications are widely used today for a variety of tasks, whether personal or professional. When looking for a service that provides face detection and classification, it is easy to find several solutions. In this project another way is described so that it is possible to perform this task according to the desired needs […]
Apr, 12

Using Machine Learning to Estimate Utilization and Throughput for OpenCL-Based SpMV Implementation on an FPGA

Hardware designers use High-Level Synthesis (HLS) tools in order to reduce the design time and design complexity. OpenCL is a framework that uses HLS tools and permits the programmer to write standardized C-like code for the host as well as for the hardware accelerators. Using OpenCL, a program can be written using different memory access […]
Apr, 12

Neural Architecture Search for Lightweight Non-Local Networks

Non-Local (NL) blocks have been widely studied in various vision tasks. However, it has been rarely explored to embed the NL blocks in mobile neural networks, mainly due to the following challenges: 1) NL blocks generally have heavy computation cost which makes it difficult to be applied in applications where computational resources are limited, and […]
Apr, 12

LUDA: Boost LSM Key Value Store Compactions with GPUs

Log-Structured-Merge (LSM) tree-based key value stores are facing critical challenges of fully leveraging the dramatic performance improvements of the underlying storage devices, which makes the compaction operations of LSM key value stores become CPU-bound, and slow compactions significantly degrade key value store performance. To address this issue, we propose LUDA, an LSM key value store […]
Apr, 5

Deep Learning for Compilers

Constructing compilers is hard. Optimising compilers are multi-million dollar projects spanning years of development, yet remain unable to fully exploit the available performance, and are prone to bugs. The rapid transition to heterogeneous parallelism and diverse architectures has raised demand for aggressively-optimising compilers to an all time high, leaving compiler developers struggling to keep up. […]
Apr, 5

Parallelization of the Honeybee Search Algorithm for Object Tracking

Object tracking refers to the relocation of specific objects in consecutive frames of a video sequence. Presently, this visual task is still considered an open research issue, and the computer science community attempted solutions from the standpoint of methodologies, algorithms, criteria, benchmarks, and so on. This article introduces a GPU-parallelized swarm algorithm, called the Honeybee […]
Apr, 5

PyMatting: A Python Library for Alpha Matting

An important step of many image editing tasks is to extract specific objects from an image in order to place them in a scene of a movie or compose them onto another background. Alpha matting describes the problem of separating the objects in the foreground from the background of an image given only a rough […]
Apr, 5

Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO

The recent introduction of powerful embedded graphics processing units (GPUs) has allowed for unforeseen improvements in real-time computer vision applications. It has enabled algorithms to run onboard, well above the standard video rates, yielding not only higher information processing capability, but also reduced latency. This work focuses on the applicability of efficient low-level, GPU hardware-specific […]
Apr, 5

Understanding GPU-Based Lossy Compression for Extreme-Scale Cosmological Simulations

To help understand our universe better, researchers and scientists currently run extreme-scale cosmology simulations on leadership supercomputers. However, such simulations can generate large amounts of scientific data, which often result in expensive costs in data associated with data movement and storage. Lossy compression techniques have become attractive because they significantly reduce data size and can […]
Mar, 29

Large-Scale Data Computing Performance Comparisons on SYCL Heterogeneous Parallel Processing Layer Implementations

Today, many big data applications require massively parallel tasks to compute complicated mathematical operations. To perform parallel tasks, platforms like CUDA (Compute Unified Device Architecture) and OpenCL (Open Computing Language) are widely used and developed to enhance the throughput of massively parallel tasks. There is also a need for high-level abstractions and platform-independence over those […]
Mar, 29

Characterizing Optimizations to Memory Access Patterns using Architecture-Independent Program Features

High-performance computing developers are faced with the challenge of optimizing the performance of OpenCL workloads on diverse architectures. The Architecture-Independent Workload Characterization (AIWC) tool is a plugin for the Oclgrind OpenCL simulator that gathers metrics of OpenCL programs that can be used to understand and predict program performance on an arbitrary given hardware architecture. However, […]

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