#openvino

Q1 2025 saw the launch of Intel's 15th-generation Arrow Lake-S desktop CPU, the Core Ultra 9 285T. Due to its advanced AI and energy efficiency design, professionals and enthusiasts seeking high performance in a low-power package will like it.

Performance

Intel Core Ultra 9 285T excels in several tasks:

Productivity & Multitasking: Its 24-core setup lets you multitask and manage demanding apps easily.

The embedded NPU supports WebNN, WindowsML, DirectML, ONNX RT, and OpenVINO, improving machine learning and AI workloads.

Graphics: Intel Arc Graphics supports 8K at 60Hz displays with DisplayPort 2.1 and HDMI 2.1.

The Energy Efficiency

The energy-efficient 285T is ideal for tiny PCs and thermally controlled systems with its 35W base power and 112W turbo power.

Platform/Compatibility Considerations

Motherboards: Z890, B860, and H810 chipsets and LGA 1851 motherboards are needed. Supports DDR5 memory up to 256 GB, ECC, and 6400 MT/s.

OS: Compatible with Windows 11 and several AI software frameworks.

Intel 285t core ultra 9 processor pricing

The Intel Core Ultra 9 285T processor will be available in some places in May 2025.

In conclusion

Improve AI/ML Application Performance with Intel VTune Profiler.

Find out how to profile Data Parallel Python and OpenVINO workloads using the oneAPI-powered tool. The use of AI and ML is rising in healthcare and life sciences, marketing and finance, manufacturing, robotics, driverless automobiles, smart cities, and more. ML workloads in real-world fields employ deep learning frameworks like PyTorch, TensorFlow, Keras, and others.

Through the “write once, deploy anywhere” approach, other developer tools, such as the OpenVINO Toolkit, also help to expedite AI research on the newest hardware architectures in fields like computer vision and generative artificial intelligence (GenAI). The goal of the open source OpenVINO Toolkit, which was first released in 2018, has been to speed up AI inference with reduced latency and increased throughput while preserving accuracy, minimizing model footprint, and maximizing hardware utilization.

It is challenging to locate and examine performance bottlenecks in the underlying source code because to the intricate structure of deep learning models, which include numerous layers and non-linear functions. ML frameworks like PyTorch and TensorFlow provide native tools and profiling APIs for tracking and evaluating performance metrics at various phases of model construction.

These approaches, however, are only applicable to software functionality. This problem is addressed by the Intel VTune Profiler, which is driven by the oneAPI and offers comprehensive insights into hardware-level memory and compute bottlenecks. By doing this, performance problems are resolved and AI applications’ performance is optimized and scaled across hardware systems with different computational envelopes.

The scope of optimization for AI/ML workloads may be expanded by using Intel VTune Profiler to profile data in concurrent Python and OpenVINO programs, as you will discover in this article.

Use Intel VTune Profiler to Boost Python Application Performance

VTune Profiler may assist in profiling a Python program, as shown in a recently published recipe in the Intel VTune Profiler Cookbook.

The following are the recipe’s fundamental software requirements:

Data Parallel Extensions for Python

Vtune Profiler (version 2022 or later)

Intel Distribution for Python

Compiler for Intel OneAPI DPC++/C++

The NumPy implementation covered in the recipe divides the calculations into logical jobs using the Intel Instrumentation and Tracing Technology (ITT) APIs and performs distance computations using the Intel oneAPI Math Kernel Library (oneMKL) routines. You may then determine which areas of the code need attention for necessary changes to get additional performance by using the VTune Profiler tool to examine the execution time and memory consumption of each logical job.

Details on the most CPU-intensive code segments are included in the output analysis report when Hotspots analysis is performed on the NumPy implementation. Additionally, it offers recommendations for investigating the profiler tool’s other performance analysis features, such Threading analysis for enhanced parallelism and Microarchitecture Exploration analysis for effective use of the underlying hardware.

Use the Data Parallel Extension for NumPy and Numba to Address Performance Bottlenecks

According to the Hotspots analysis report, NumPy operations and underlying oneMKL functions account for a significant amount of the execution time in the simple NumPy implementation of the pairwise distance computation example. By making little code modifications, NumPy may be swapped out for the Data Parallel Extension for NumPy, which will eliminate these bottlenecks. To evaluate the speed gains over the simple NumPy code and find any areas that might need further optimization, run the Hotspots analysis once again.

Additionally, the VTune Profiler makes recommendations such as using the Data Parallel Extension for Numba with your platform’s GPU to bring offload accelerator parallelism to the application. The Numba JIT compiler for NumPy operations has an open-source extension called Numba. It offers Python kernel programming APIs that resemble SYCL. The GPU Compute/Media Hotspots analysis preview function of VTune Profiler may then be used to examine the Numba implementation’s execution on a GPU.

Use Intel VTune Profiler to Examine OpenVINO Applications’ Performance

Using the VTune Profiler to profile OpenVINO-based AI applications is covered in another new recipe in the VTune Profiler cookbook. It discusses how to use the profiler tool to analyze performance bottlenecks in the CPU, GPU, and Neural Processing Unit (NPU).

If your OpenVINO application makes use of the Intel oneAPI Data Analytics Library (oneDAL) and/or the Intel oneAPI Deep Neural Network (oneDNN) Intel Distribution for Python Intel VTune Profiler (v2024.1 or later), you can access the Benchmark Tool application as part of the OpenVINO Toolkit Intel oneAPI Base Toolkit.

The recipe offers detailed steps for configuring OpenVINO with the ITT APIs for performance analysis, building the OpenVINO source, and setting it up. It profiles the AI application and analyzes performance and latency using a reference benchmark application.

Depending on the computational architecture, you may use the VTune Profiler‘s numerous performance analysis features to find hotspots and look at how much hardware is being utilized by specific code sections.

For example,

To examine CPU bottlenecks that is, the sections of code that take up the most CPU execution time use the Hotspots Analysis tool.

Use the GPU Compute/Media Hotspots Analysis preview function to profile GPU hotspots. Examine inefficient kernel methods, examine the frequency of GPU instructions for various kinds of instructions, and more to get an understanding of GPU use.

The AI PCs’ Neural Processing Units (NPUs) are made especially to boost AI/ML applications’ performance. With the Intel Distribution of OpenVINO Toolkit, you may transfer compute-intensive AI/ML tasks to Intel NPUs. You may examine the NPU performance using a number of hardware measures, including workload size, execution time, sampling interval, and more, with the use of the VTune Profiler‘s NPU Exploration Analysis preview function.

Intel VTune Profiler Download

Use one of the following methods to install Intel VTune Profiler on your computer:

Get the standalone bundle here.

As part of the Intel oneAPI Base Toolkit, download Intel VTune Profiler.

Know the Process

In the standard software performance analysis process,Image Credit To Intel

Use one of the following methods to launch Intel VTune Profiler:

Using Microsoft Visual Studio’s GUI From the Command Line

Configure parameters and choose a profiling analysis for your application.

Create a profile of the target system (remote collection) or application on the host (local collection).

View the host system’s findings.

Identify bottlenecks and address them over a number of cycles until you reach a desirable level of performance.

FAQs

What does Intel VTune do?

Examine CPU usage, OpenMP efficiency, memory access, and vectorization to assess application performance. Measure metrics to find memory access concerns.

Concerning DeepScenario

DeepScenario, an Intel Ignite and Intel Liftoff startup, uses artificial intelligence (AI) to establish the crucial connection between the real and virtual worlds, enabling industry-specific solutions in dynamic contexts. This enables businesses like Volkswagen, Bosch, and Torc Robotics to establish a continuous loop that involves observing the real world, comprehending its dynamics, and applying this understanding to their unique applications, like autonomous driving.

A basic computer vision programme from DeepScenario is utilised to do this. Videos of the real world are transformed into a dynamic virtual counterpart using highly developed algorithms and commercially available cameras, laying the groundwork for the related application.

The Problem Applications requiring a dynamic digital counterpart of the world are multiplying. This makes it possible to automate processes, cut expenses, and increase the functional scope. Digital twins of road traffic are required, for example, in order to test car software in a simulated environment and advance autonomous driving.

Another example is the transportation sector, which uses real-time digital twins to control traffic in cities and monitor parking space occupancy. Significant obstacles stand in the way of current techniques, such as limited application due to simplifying assumptions or limited scalability because of costly sensor sets.

The Resolution This is the exact situation that Stuttgart-based AI startup DeepScenario finds itself in. Using its 3D computer vision software, DeepScenario creates dynamic virtual worlds out of films. Because it uses widely available cameras and a 3D methodology to assure generalisation, the solution is incredibly scalable.

For example, the automotive sector makes use of DeepScenario’s AI technology. Here, videos from cars, traffic cameras, or drones are transformed into dynamic digital twins of road traffic using the industry-leading algorithms from DeepScenario. Next, new car software is trained and tested in these virtual environments through simulation.

The software developed by DeepScenario can also be applied to transportation-related applications. This involves using already-installed cameras to precisely monitor parking lots, for instance. All objects in the scene are immediately recognised by the programme, which tracks them over time with centimetre accuracy in 3D. DeepScenario’s algorithms, in contrast to current 2D-based techniques, can accurately ascertain the position, orientation, and dimensions of the objects in 3D without the use of extra sensors like LiDAR. They can use this to determine possible parking infractions or infer the occupancy of a parking place in real time.

Additionally, DeepScenario’s software can be used to develop entirely new services, such as automated valet parking. Such a service was just introduced by Bosch Mobility.

With DeepScenario’s AI technology, numerous new use cases are possible, such as applications for logistics or intelligent traffic control.

DeepScenario for Edge Intel Tools Intel has made it possible for its software to run in real time on edge servers in a very short amount of time by using Intel tools. With over a billion fixed cameras and a volume of roughly $15 billion, this creates a market for us that is likely to develop.

What is OpenVINO The optimised interface of DeepScenario’s software with Intel hardware which makes use of tools like the Intel OpenVINO Toolkit is another advantage. The inference of AI applications on Intel hardware is accelerated by this open-source software, particularly when creating and utilising deep learning-based AI models like those from DeepScenario.

Intel OpenVINO Increasing the Usability of Generative AI in Practical Situations​ The OpenVINO toolbox is an open-source toolkit that maintains accuracy, minimises model footprint, and maximises hardware utilisation while speeding up AI inference with lower latency and faster throughput. Large language models (LLM), generative AI, computer vision, and deep learning integration are among the areas it simplifies and integrates more easily.​

Intel Geti Platform With the help of this for-profit software platform, enterprise teams can create vision AI models more quickly. Businesses may use the platform to create models with little data, and by integrating OpenVINO, it makes it easier to deploy solutions widely.

Software & Solutions Catalogue for AI Inference Examine ISV solutions built on OpenVINO when you’re prepared to launch your solution. This ebook is divided into parts, like banking or healthcare, to make it easier for you to traverse the options table and select the solution that best fits your use-case demands.

In actuality, DeepScenario and Intel SceneScape work together to provide a smart parking solution that is a shining example of their teamwork. This method uses just existing monocular cameras to build realistic 4D digital twins of parking lots by utilising the OpenVINO toolset. Apart from surmounting the constraints of conventional techniques, it is also applicable to a range of uses such as intelligent traffic control, port surveillance, and Industry 4.0 programmes.

Additionally, DeepScenario and the autonomous driving area benefit greatly from Intel’s funding of the Carla Simulator’s development.

Intel Ignite With the help of Intel, Intel Ignite, and the Intel Liftoff programme for AI companies, DeepScenario is setting new standards in the creation of dynamic virtual environments from real-world recordings thanks to its AI technology. The methodology employed by DeepScenario is very scalable and has numerous applications. Utilising 3D algorithms creates new technological opportunities, of which DeepScenario has already shown to be indispensable in the transportation and automotive sectors.

OpenVINO Integration with TensorFlow

Accelerate the performance of your TensorFlow models with the help of OpenVINO™ Integration with TensorFlow Source link