Blog Posts

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Using Nsight Compute or Nvprof to Show Mixed Precision Use in Deep Learning Models

This blog post was originally published at NVIDIA's website. It is reprinted here with the permission of NVIDIA. Mixed precision combines different numerical precisions in a computational method. The Volta and Turing generation of GPUs introduced Tensor Cores, which provide significant throughput speedups over single precision math pipelines. Deep learning networks can be trained with […]

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Introducing INT8 Quantization for Fast CPU Inference Using OpenVINO

This blog post was originally published at Intel's website. It is reprinted here with the permission of Intel. Deep learning framework optimizations and tools that streamline deployment are advancing the adoption of inference applications on Intel® platforms. Reducing model precision is an efficient way to accelerate inference on processors that support low precision math, with

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Seeing the Wood for the Trees: Using AI for Smarter Forest Management

This blog post was originally published at NVIDIA's website. It is reprinted here with the permission of NVIDIA. Nearly 19 million acres of forests are destroyed annually, equal to 27 football pitches a minute. Forests serve as homes for thousands of animals, and for many people they’re a source of food, water, clothing, medicine and

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Using Autonomous Vehicle Technology to Make Roads Safer Today

This blog post was originally published at Intel's website. It is reprinted here with the permission of Intel. Technologies Developed for Fully Autonomous Vehicles Can Improve the Advanced Driver Assistance Systems Already in Wide Use Safety has always been our North Star. We view it as a moral imperative to pursue a future with autonomous

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Scalable Methods for 8-bit Training of Neural Networks

This blog post was originally published at Intel's website. It is reprinted here with the permission of Intel. Quantized neural networks (QNNs) are regularly used to improve network efficiency in deep learning. Though there has been much research into different quantization schemes, the number of bits required and the best quantization scheme is still unknown.

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HE-Transformer for nGraph: Enabling Deep Learning on Encrypted Data

This blog post was originally published at Intel's website. It is reprinted here with the permission of Intel. We are pleased to announce the open source release of HE-Transformer, a homomorphic encryption (HE) backend to nGraph, Intel’s neural network compiler. HE allows computation on encrypted data. This capability, when applied to machine learning, allows data

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