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Wednesday, January 29, 2020 9:00 am PST
#SystemsIoT

What's New in Intel® System Studio 2020?

The new calendar year marks Year 7 of Intel® System Studio, a suite purpose-built for developers who focus on the interplay between HPC system bring-up and the data-centric apps that run on them. Find out how the 2020 release still has your back.

February 2020 marks Intel® System Studio’s 7th year of helping device manufacturers, system integrators, and IoT application developers more quickly go from prototype to product—this includes accelerating development of connected devices, boosting performance and power efficiency, and strengthening system reliability.

The new release keeps it going, with improvements and advancements to support your 2020 needs.

Join senior technical consulting engineer Jeff Reinemann for a comprehensive overview of the latest suite, including these highlights:

  • Intel® VTune™ Profiler’s new capability (called Intel® Processor Trace) to identify performance issues by capturing exact trace and instruction breakdowns
  • Performance bottleneck analysis in storage and networking systems that leverage the open source Storage Performance Development Kit (SPDK) or Data Plane Development Kit (DPDK)
  • A tour of the newly designed Intel® System Debugger interface

Register now.

Get the software
Be sure to download the 2020 edition of Intel® System Studio, FREE. (The free license is backed by community forum support.)

Jeffrey Reinemann, Technical Consulting Engineer, Intel Corporation

Jeffrey is a Technical Consulting Engineer at Intel responsible for supporting key Intel® System Studio tool suite components—including its energy and performance profilers and analyzers. In his 26 years with the company, Jeffrey has been involved in numerous software technology strategies and solutions, including Intel® Software Asset Manager, anti-theft technology, prototyping and analysis of PC usage model solutions, client PC management and power management architecture, and quality assurance programs for real-time operating systems and PC BIOS. Jeffrey has a M.S. in Computer Sciences from the University of Wisconsin at Madison, and a B.S. in Computer Sciences and Mathematics from Carroll University in Waukesha, WI.

Wednesday, February 12, 2020 9:00 am PST
#VisualComputing

Goodbye, Slow Inference Workloads. Hello, Improved Quantization Techniques.

Experiencing sluggish performance for DL-at-the-edge workloads? New enhancements in the Intel® Distribution of OpenVINO™ toolkit might be just what your inference-dependent apps need, including on multiple architectures. Sign up to find out why … and how.

Deep learning deployment on the edge for real-time inference can significantly reduce the cost of communicating with the cloud in terms of network bandwidth, network latency, and power consumption.

But there’s a flip side: Edge devices have limited memory, compute, and power. As a result, using the traditional 32-bits of floating-point precision is often too computationally heavy for embedded deep learning inference workloads.

The Intel® Distribution of OpenVINO™ toolkit offers a solution via INT8 quantization—deep learning inference with 8-bit multipliers.

Join deep learning expert Alex Kozlov for a deeper dive into achieving better performance with less overhead on Intel® CPUs, GPUs, and VPUs using OpenVINO™ toolkit’s latest INT8 Calibration Tool and Runtime. He’ll cover:

  • New features such as asymmetric quantization, bias correction, and weight equalization to improve quality of inference workloads and lower precision
  • How to make best use of OpenVINO’s enhanced capabilities for your AI applications
  • Using INT8 to accelerate computation performance and save memory bandwidth and power, and provide better cache locality

Register now.

Get the software
Download the latest version of Intel® Distribution of OpenVINO™ toolkit so you can follow along during the webinar.

More resources

Alexander Kozlov, Deep Learning R&D Engineer, Intel Corporation

Alexander is a Machine Learning/Deep Learning (ML/DL) Engineer at Intel with expertise in DL object detection architectures, Human Action Recognition approaches, and Neural Network compression techniques.

Before Intel, he was a senior software engineer and researcher at Itseez (now acquired by Intel) where he worked on Computer VIsion algorithms for ADAS systems. Now Alexander focuses on deep learning neural network (DNN) compression methods and tools which allow getting more lightweight and hardware-friendly models.

Alex holds a Master’s Degree from University of Nizhni Novgorod.

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