Search Results for "power basic"

This software was previously known as Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) and Deep Neural Network Library (DNNL). oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform performance library of basic building blocks for deep learning applications. oneDNN is part of oneAPI. The library is optimized for Intel(R) Architecture Processors, Intel Processor Graphics and Xe Architecture graphics. oneDNN has experimental support for the following...

... is used for data augmentation to improve machine learning models and to help power features like Spotify’s AI DJ and AI Voice Translation. pedalboard also helps in the process of content creation, making it possible to add effects to audio without using a Digital Audio Workstation.

AtomAI is a Pytorch-based package for deep and machine-learning analysis of microscopy data that doesn't require any advanced knowledge of Python or machine learning. The intended audience is domain scientists with a basic understanding of how to use NumPy and Matplotlib. It was developed by Maxim Ziatdinov at Oak Ridge National Lab. The purpose of the AtomAI is to provide an environment that bridges the instrument-specific libraries and general physical analysis by enabling the seamless...

We humans are bound by intellectual abilities. All knowledge is far beyond power of any person. The only way to apply knowledge is to build machines able to present it human way but not limited by volume. Intellect Modeling Kit (IMK) is intended to build knowledge machines (KM) assisting experts on the steps of activity: * Observation; * Producing propositions based on knowledge; * Elimination of impossible propositions; * Selection and verification of the most appropriate propositions...

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Low-power approximate adders provide basic building blocks for approximate computing hardware that have shown remarkable energy efficiency for error-resilient applications (like image/video processing, computer vision, etc.), especially for battery-driven portable systems. In this paper, we present a novel scalable, fast yet accurate analytical method to evaluate the output error probability of multi-bit low power adders for a predetermined probability of input bits. Our method recursively...