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Intel® Deep Learning Boost (Intel® DL Boost)

The second generation of Intel® Xeon® Scalable processors introduced a collection of features for deep learning, packaged together as Intel® DL Boost. These features include Vector Neural Network Instructions (VNNI), which increases throughput for inference applications with support for int8 convolutions by combining multiple machine instructions from previous generations into one machine instruction.

First MLPerf Inference Results

Technical Description of VNNI

Frameworks and Tools

These frameworks and tools include support for Intel DL Boost on second and third generation Intel Xeon Scalable processors.

TensorFlow*

Accelerate Inference with Intel® Deep Learning

MXNet

AWS Launches New Amazon EC2 C5 Instances Featuring Intel® DL Boost Technology

Get Started with Intel® Optimization for MXNet*

Intel® Distribution of OpenVINO™ Toolkit

Introducing int8 Quantization for Fast CPU Inference

Guide for Inference in FP32 and int8

Model Quantization

Most deep learning models are built using 32 bits floating-point precision (FP32). Quantization is the process to represent the model using less memory with minimal accuracy loss. In this context, the main focus is the representation in int8.

Code Sample: New Deep Learning Instruction (bfloat16) Intrinsic Functions

Learn how to use the new Intel® Advanced Vector Extensions 512 with Intel® DL Boost in the third generation of Intel Xeon Scalable processors.

Low-Precision int8 Inference Workflow

Get an explanation of the model quantization steps using the Intel® Distribution of OpenVINO™ toolkit.

Customer Use Cases

Siemens Healthineers and Intel Demonstrate the Potential of AI for Real-Time Cardiac MRI Diagnosis

iFLYTEK Optimizes AI Workloads on Intel Xeon Scalable Processors

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