Dec, 26

NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems

We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics. NetKet is built around neural-network quantum states and provides efficient algorithms for their evaluation and optimization. This new version is built on top of JAX, a differentiable programming and accelerated linear algebra framework for the Python programming language. The most significant […]
Dec, 19

Optimization of Compiler-generated OpenCL CNN Kernels and Runtime for FPGAs

This work explores the viability of end-to-end convolutional neural network inference using OpenCL HLS kernels generated from TVM on Intel FPGAs. We explore layer-pipelined execution for small networks and time-multiplexed kernels for larger CNNs. Naively generated kernels do not produce efficient hardware. We propose a set of optimizations to increase parallelism, resource utilization, and more […]
Dec, 19

TCUDB: Accelerating Database with Tensor Processors

The emergence of novel hardware accelerators has powered the tremendous growth of machine learning in recent years. These accelerators deliver incomparable performance gains in processing high-volume matrix operators, particularly matrix multiplication, a core component of neural network training and inference. In this work, we explored opportunities of accelerating database systems using NVIDIA’s Tensor Core Units […]
Dec, 19

N-Cloth: Predicting 3D Cloth Deformation with Mesh-Based Networks

We present a novel mesh-based learning approach (N-Cloth) for plausible 3D cloth deformation prediction. Our approach is general and can handle cloth or obstacles represented by triangle meshes with arbitrary topology. We use graph convolution to transform the cloth and object meshes into a latent space to reduce the non-linearity in the mesh space. Our […]
Dec, 19

Evaluation of Pseudo-Random Number Generation on GPU Cards

Monte Carlo methods rely on sequences of random numbers to obtain solutions to many problems in science and engineering. In this work, we evaluate the performance of different pseudorandom number generators (PRNGs) of the Curand library on a number of modern Nvidia GPU cards. As a numerical test, we generate pseudo-random number (PRN) sequences and […]
Dec, 19

On the accuracy and performance of the lattice Boltzmann method with 64-bit, 32-bit and novel 16-bit number formats

Fluid dynamics simulations with the lattice Boltzmann method (LBM) are very memory-intensive. Alongside reduction in memory footprint, significant performance benefits can be achieved by using FP32 (single) precision compared to FP64 (double) precision, especially on GPUs. Here, we evaluate the possibility to use even FP16 and Posit16 (half) precision for storing fluid populations, while still […]
Dec, 12

Fast Neural Representations for Direct Volume Rendering

Despite the potential of neural scene representations to effectively compress 3D scalar fields at high reconstruction quality, the computational complexity of the training and data reconstruction step using scene representation networks limits their use in practical applications. In this paper, we analyze whether scene representation networks can be modified to reduce these limitations and whether […]
Dec, 12

Manas: Mining Software Repositories to Assist AutoML

Today deep learning is widely used for building software. A software engineering problem with deep learning is that finding an appropriate convolutional neural network (CNN) model for the task can be a challenge for developers. Recent work on AutoML, more precisely neural architecture search (NAS), embodied by tools like Auto-Keras aims to solve this problem […]
Dec, 12

CitiusSynapse: A Deep Learning Framework for Embedded Systems

As embedded systems, such as smartphones with limited resources, have become increasingly popular, active research has recently been conducted on performing on-device deep learning in such systems. Therefore, in this study, we propose a deep learning framework that is specialized for embedded systems with limited resources, the operation processing structure of which differs from that […]
Dec, 12

High performance computing on Android devices – a case study

High performance computing for low power devices can be useful to speed up calculations on processors that use a lower clock rate than computers for which energy efficiency is not an issue. In this trial, different high performance techniques for Android devices have been compared, with a special focus on the use of the GPU. […]
Dec, 12

GPU backed Data Mining on Android Devices

Choosing an appropriate programming paradigm for high-performance computing on low-power devices can be useful to speed up calculations. Many Android devices have an integrated GPU and – although not officially supported – the OpenCL framework can be used on Android devices for addressing these GPUs. OpenCL supports thread and data parallelism. Applications that use the […]
Dec, 5

Analysis and Comparison of Performance and Power Consumption of Neural Networks on CPU, GPU, TPU and FPGA

In this work, we analyze the performance of neural networks on a variety of heterogenous platforms. We strive to find the best platform in terms of raw benchmark performance, performance per watt and performance per Euro. To reach this goal, we focused on convolutional neural networks and created several micro- and macrobenchmark applications and used […]

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