#onnx

[govindhtech]

How ONNX Runtime is Evolving AI in Microsoft with Intel

With the goal of bringing AI features to devices, the Microsoft Office team has been working with Intel and ONNX Runtime for over five years to integrate AI capabilities into their array of productivity products. The extension of AI inference deployment from servers to Windows PCs enhances responsiveness, preserves data locally to protect privacy, and increases the versatility of AI tooling by removing the requirement for an internet connection. These advancements keep powering Office features like neural grammar checker, ink form identification, and text prediction.

What is ONNX Runtime

As a result of their extensive involvement and more than two decades of cooperation, Intel and Microsoft are working more quickly to integrate AI features into Microsoft Office for Windows platforms. The ONNX Runtime, which enables machine learning models to scale across various hardware configurations and operating systems, is partially responsible for this accomplishment. The ONNX runtime is continuously refined by Microsoft, Intel, and the open-source community. When used in this way, it enhances the efficiency of Microsoft Office AI models running on Intel platforms.

AI Generative

With ONNX Runtime, you can incorporate the power of large language models (LLMs) and generative artificial intelligence (AI) into your apps and services. State-of-the-art models for image synthesis, text generation, and other tasks can be used regardless of the language you develop in or the platform you need to run on.

ONNX Runtime Web

With a standard implementation, ONNX Runtime Web enables cross-platform portability for JavaScript developers to execute and apply machine learning models in browsers. Due to the elimination of the need to install extra libraries and drivers, this can streamline the distribution process.

ONNX Runtime Java

Using the same API as cloud-based inferencing, ONNX Runtime Mobile runs models on mobile devices. Swift, Objective-C, Java, Kotlin, JavaScript, C, and C++ developers can integrate AI to Android, iOS, react-native, and MAUI/Xamarin applications by using their preferred mobile language and development environment.

ONNX Runtime Optimization

Inference models from various source frameworks (PyTorch, Hugging Face, TensorFlow) may be efficiently solved by ONNX Runtime on various hardware and software stacks. In addition to supporting APIs in many languages (including Python, C++, C#, C, Java, and more), ONNX Runtime Inference leverages hardware accelerators and functions with web browsers, cloud servers, and edge and mobile devices.

Ensuring optimal on-device AI user experience necessitates ongoing hardware and software optimization, coordinated by seasoned AI-versed experts. The most recent ONNX Runtime capabilities are regularly added to Microsoft Office’s AI engine, guaranteeing optimal performance and seamless AI model execution on client devices.

Intel and Microsoft Office have used quantization, an accuracy-preserving technique for optimizing individual AI models to employ smaller datatypes. “Microsoft Office’s partnership with Intel on numerous inference projects has achieved notable reductions in memory consumption, enhanced performance, and increased parallelization all while maintaining accuracy by continuing to focus on our customers,” stated Joshua Burkholder, Principal Software Engineer of Microsoft’s Office AI Platform.

With the help of Intel’s DL Boost, a collection of specialized hardware instruction sets, this method reduces the on-device memory footprint, which in turn reduces latency. The ONNX Runtime has been tuned to work with Intel’s hybrid CPU design, which combines efficiency and performance cores. With Intel Thread Director, this is further enhanced by utilising machine learning to schedule activities on the appropriate core, guaranteeing that they cooperate to maximise performance-per-watt.

Furthermore, on-device AI support for Office web-based experiences is being provided by Intel and Microsoft in partnership. The ONNX Runtime Web makes this feasible by enabling AI feature support directly in web applications, like Microsoft Designer.

Balancing Cloud and On-device

With the advent of AI PCs, particularly those featuring the latest Intel Core Ultra processor, more workloads are being able to move from cloud-based systems to client devices. Combining CPU ,  GPU , and NPU , Intel Core Ultra processors offer complementary AI compute capabilities that, when combined with model and software optimizations, can be leveraged to provide optimal user experience.

Even while the AI PC opens up new possibilities for executing AI activities on client devices, it is necessary to assess each model separately to ascertain whether or not running locally makes sense. AI computation may take on a hybrid form in the future, with a large number of models running on client devices and additional cloud computing used for more complicated tasks. In order to aid with this, Intel AI PC development collaborates with the Office team to determine which use cases are most appropriate for customers using the Intel Core Ultra processor.

The foundation of Intel and Microsoft’s continued cooperation is a common goal of an AI experience optimized to span cloud and on-device with products such as AI PC. Future Intel processor generations will enhance the availability of client compute for AI workloads. As a result, Intel may anticipate that essential tools like Microsoft Office will be created to provide an excellent user experience by utilizing the finest client and cloud technologies.

Read more on govindhtech.com

[thriftrescue]

ONE PIECE shoes at thrift! "ONNX FASHION XIUXIAN" "OFF XIUXIANFASHION" 3D2Y

[hackernewsrobot]

ONNX: The Open Standard for Seamless Machine Learning Interoperability

https://github.com/onnx/onnx

[thursdayisbetterthanfriday]

Building A Responsive Game AI - Part 4

The part where the deadline is in less than a fortnight and the parts don't all fit together as planned.

The unfortunate nature of working with relatively new software ideas is that many existing systems are incompatible. "How can this be?" you might ask, "surely you can just refactor existing code to make it compatible?" "Coding is one of the few disciplines where you can mould tools to your will, right?" This is true - you can do so.

It does, however, take time and energy and often requires learning that software's complexities to a level where you spend half as much time re-working existing software as it takes to make the new software. Using AI in game engines, for example, has been a challenge. Unity Engine does have an existing package to handle AI systems called "Barracuda". On paper, it's a great system, that allows ONNX based AI models to run natively within a Unity game environment. You can convert AI models trained in the main AI field software libraries into ONNX and use them in Unity. The catch is that it doesn't have fully compatibility with all AI software library functions. This is a problem with complex transformer based AI models specifically - aka. this project. Unity does have an upcoming closed-beta package which will resolve this (Sentis), but for now this project will effectively have to use a limited system of local network sockets to interface the main game with a concurrently run Python script. Luckily I'd already made this networking system since Barracuda doesn't allow model training within Unity itself and I needed a system to export training data and re-import re-trained models back into the engine.

People don't often realise how cobbled together software systems can be. It's the creativity, and lateral thinking, that make these kinds of projects interesting and challenging.

[ingoampt]

ONNX vs. Core ML: Choosing the Best Approach for Model Conversion in 2024

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[marcoluther]

What Are the Most Popular AI Development Tools in 2025?

As artificial intelligence (AI) continues to evolve, developers have access to an ever-expanding array of tools to streamline the development process. By 2025, the landscape of AI development tools has become more sophisticated, offering greater ease of use, scalability, and performance. Whether you're building predictive models, crafting chatbots, or deploying machine learning applications at scale, the right tools can make all the difference. In this blog, we’ll explore the most popular AI development tools in 2025, highlighting their key features and use cases.

1. TensorFlow

TensorFlow remains one of the most widely used tools in AI development in 2025. Known for its flexibility and scalability, TensorFlow supports both deep learning and traditional machine learning workflows. Its robust ecosystem includes TensorFlow Extended (TFX) for production-level machine learning pipelines and TensorFlow Lite for deploying models on edge devices.

Key Features:

Extensive library for building neural networks.

Strong community support and documentation.

Integration with TensorFlow.js for running models in the browser.

Use Case: Developers use TensorFlow to build large-scale neural networks for applications such as image recognition, natural language processing, and time-series forecasting.

2. PyTorch

PyTorch continues to dominate the AI landscape, favored by researchers and developers alike for its ease of use and dynamic computation graph. In 2025, PyTorch remains a top choice for prototyping and production-ready AI solutions, thanks to its integration with ONNX (Open Neural Network Exchange) and widespread adoption in academic research.

Key Features:

Intuitive API and dynamic computation graphs.

Strong support for GPU acceleration.

TorchServe for deploying PyTorch models.

Use Case: PyTorch is widely used in developing cutting-edge AI research and for applications like generative adversarial networks (GANs) and reinforcement learning.

3. Hugging Face

Hugging Face has grown to become a go-to platform for natural language processing (NLP) in 2025. Its extensive model hub includes pre-trained models for tasks like text classification, translation, and summarization, making it easier for developers to integrate NLP capabilities into their applications.

Key Features:

Open-source libraries like Transformers and Datasets.

Access to thousands of pre-trained models.

Easy fine-tuning of models for specific tasks.

Use Case: Hugging Face’s tools are ideal for building conversational AI, sentiment analysis systems, and machine translation services.

4. Google Cloud AI Platform

Google Cloud AI Platform offers a comprehensive suite of tools for AI development and deployment. With pre-trained APIs for vision, speech, and text, as well as AutoML for custom model training, Google Cloud AI Platform is a versatile option for businesses.

Key Features:

Integrated AI pipelines for end-to-end workflows.

Vertex AI for unified machine learning operations.

Access to Google’s robust infrastructure.

Use Case: This platform is used for scalable AI applications such as fraud detection, recommendation systems, and voice recognition.

5. Azure Machine Learning

Microsoft’s Azure Machine Learning platform is a favorite for enterprise-grade AI solutions. In 2025, it remains a powerful tool for developing, deploying, and managing machine learning models in hybrid and multi-cloud environments.

Key Features:

Automated machine learning (AutoML) for rapid model development.

Integration with Azure’s data and compute services.

Responsible AI tools for ensuring fairness and transparency.

Use Case: Azure ML is often used for predictive analytics in sectors like finance, healthcare, and retail.

6. DataRobot

DataRobot simplifies the AI development process with its automated machine learning platform. By abstracting complex coding requirements, DataRobot allows developers and non-developers alike to build AI models quickly and efficiently.

Key Features:

AutoML for quick prototyping.

Pre-built solutions for common business use cases.

Model interpretability tools.

Use Case: Businesses use DataRobot for customer churn prediction, demand forecasting, and anomaly detection.

7. Apache Spark MLlib

Apache Spark’s MLlib is a powerful library for scalable machine learning. In 2025, it remains a popular choice for big data analytics and machine learning, thanks to its ability to handle large datasets across distributed computing environments.

Key Features:

Integration with Apache Spark for big data processing.

Support for various machine learning algorithms.

Seamless scalability across clusters.

Use Case: MLlib is widely used for recommendation engines, clustering, and predictive analytics in big data environments.

8. AWS SageMaker

Amazon’s SageMaker is a comprehensive platform for AI and machine learning. In 2025, SageMaker continues to stand out for its robust deployment options and advanced features, such as SageMaker Studio and Data Wrangler.

Key Features:

Built-in algorithms for common machine learning tasks.

One-click deployment and scaling.

Integrated data preparation tools.

Use Case: SageMaker is often used for AI applications like demand forecasting, inventory management, and personalized marketing.

9. OpenAI API

OpenAI’s API remains a frontrunner for developers building advanced AI applications. With access to state-of-the-art models like GPT and DALL-E, the OpenAI API empowers developers to create generative AI applications.

Key Features:

Access to cutting-edge AI models.

Flexible API for text, image, and code generation.

Continuous updates with the latest advancements in AI.

Use Case: Developers use the OpenAI API for applications like content generation, virtual assistants, and creative tools.

10. Keras

Keras is a high-level API for building neural networks and has remained a popular choice in 2025 for its simplicity and flexibility. Integrated tightly with TensorFlow, Keras makes it easy to experiment with different architectures.

Key Features:

User-friendly API for deep learning.

Modular design for easy experimentation.

Support for multi-GPU and TPU training.

Use Case: Keras is used for prototyping neural networks, especially in applications like computer vision and speech recognition.

Conclusion

In 2025, AI development tools are more powerful, accessible, and diverse than ever. Whether you’re a researcher, a developer, or a business leader, the tools mentioned above cater to a wide range of needs and applications. By leveraging these cutting-edge platforms, developers can focus on innovation while reducing the complexity of building and deploying AI solutions.

As the field of AI continues to evolve, staying updated on the latest tools and technologies will be crucial for anyone looking to make a mark in this transformative space.

[willwade]

Been working with Sherpa-Onnx TTS a lot over the last year. It’s a nice project to make a onnx runtime for lots of different languages and interfaces. Just whipped together a Gradio demo to show all the voices and hear them - most notably MMS Onnx models Sherpa-Onnx Demo

[lacyc3]

Van a Scaleway-nél RISC-V-ös, fizikai gép Alibaba TH-1520-as processzorral (4 mag, 4 szál), 16GB memóriával, 128GB MMC memóriával havi 16 eur + áfa árban. Fut rajta az Ubuntu 24.04:

SoC T-Head 1520CPU (C910) RV64GC 4 cores 1,85 GHz GPU (OpenCL 1.1/1.2/2.0, OpenGL ES 3.0/3.1/3.2, Vulkan 1.1/1.2, Android NN HAL) VPU (H.265/H.264/VP9 video encoding/decoding) NPU (4TOPS@INT8 1GHz, Tensorflow, ONNX, Caffe) Évekkel ezelőtt az ARM-et szerettem, lényegében a teljes oldal egy 2 gyufás doboz méretű fizikai vason futott.

Viszont ehhez nekem most keresni kéne problémát, amit meg kell oldani.. :D

Fele ennyiért ugranék, mint gyöngytyúk a takonyra, de így nem éri meg csak "for-fun" szórakozni egy új architektúrával, ami mellett kéne valami backup is, mert 0% az SLA.

[codingprolab]

Solved Homework 5 COMS E6998  Problem 1 - SSD, ONNX model, Visualization, Inferencing 35 points In this problem we will be inferencing SSD ONNX

Problem 1 – SSD, ONNX model, Visualization, Inferencing 35 points In this problem we will be inferencing SSD ONNX model using ONNX Runtime Server. You will follow the github repo and ONNX tutorials (links provided below). You will start with a pretrained Pytorch SSD model and retrain it for your target categories. Then you will convert this Pytorch model to ONNX and deploy it on ONNX runtime…

[b2bcybersecurity]

Machine Learning oder Maschinelles Lernen (ML) ist in der Softwareentwicklung unverzichtbar geworden und ermöglicht schnellere Erkennung, verbesserte Automatisierung und datenbasierte Anwendungen. JFrogs jüngste Untersuchung legt eine Reihe von Schwachstellen in verbreiteten Machine Learning Frameworks offen, die Unternehmen potenziellen Angriffen aussetzen. Die Analyse verdeutlicht, wie wichtig robuste Sicherheitsmaßnahmen beim Entwickeln und Betreiben von ML-Anwendungen sind. Die dokumentierten Schwachstellen betreffen die wichtigsten ML-Plattformen und machen deutlich, wie böswillige Akteure durch gezielte Angriffstechniken die Vertraulichkeit, Integrität und Verfügbarkeit produktiver ML-Systeme gefährden könnten. Kritische Schwachstellen in PyTorch und TensorFlow. Die Open-Source-Bibliothek PyTorch enthält Schwachstellen wie CVE-2022-41978 und CVE-2023-43645, die es Angreifern ermöglichen, schädliche Daten einzuschleusen, Sicherheitsmaßnahmen zu umgehen und unbefugt auf Ressourcen zuzugreifen. Konkret ermöglicht CVE-2022-41978 Angreifern die Ausführung von Befehlen durch manipulierte Deserialisierung, indem während des Ladens des Modells schädlicher Code eingebracht wird, der die Integrität gefährdet. CVE-2023-43645 betrifft eine Path-Traversal-Schwachstelle im TorchServe-Server von PyTorch, durch die Dateien überschrieben oder beliebige Skripte auf dem Host-System ausgeführt werden könnten. Machine Learning und TensorFlow Auch TensorFlow weist kritische Sicherheitslücken auf, darunter CVE-2023-32457, die einen potenziellen Angriffsvektor für Modell-Deserialisierungs-Angriffe darstellt. Diese Schwachstelle erlaubt TensorFlow-Modellen, eine Speicherbeschädigung herbeizuführen, was Systemabstürze oder die Ausführung nicht autorisierten Codes zur Folge haben kann. Das Risiko bei der Modell-Deserialisierung wird hier besonders deutlich: Unsachgemäß behandelte Daten in diesem essenziellen Prozess können als Einstiegspunkte dienen, um ML-Umgebungen zu kompromittieren. Darüber hinaus weisen die Sicherheitsforscher auf Schwachstellen in ONNX-Modellen (Open Neural Network Exchange) hin. Durch uneingeschränkte Dateioperationen beim Laden des Modells könnte das System manipuliert werden, wodurch Angreifer auf Systemdateien zugreifen oder diese verändern können. Solche Schwachstellen bergen das Risiko unbefugter Datenzugriffe oder sogar einer vollständigen Kompromittierung des Systems. So wurden die Sicherheitslücken ausgenutzt Die Forscher beschreiben außerdem die Methoden, mit denen Angreifer die Sicherheitslücken ausnutzen können, und fokussieren dabei Bedrohungen für die Lieferkette von Machine-Learning-Diensten. Durch Angriffe auf den Deserialisierungsprozess kann bösartiger Code eingeschleust werden, der bei der Bereitstellung des Modells ausgeführt wird. Da Machine Learning weiterhin viele Sektoren durchdringt, wird das Schließen dieser Sicherheitslücken entscheidend, um sensible Daten zu schützen und robuste, sichere ML-Umgebungen aufrechtzuerhalten. Unternehmen sollten deshalb konsequent auf DevSecOps-Praktiken setzen und sicherstellen, dass Sicherheitsmaßnahmen integraler Bestandteil der Bereitstellung und Verwaltung von ML-Modellen bleiben.   Über JFrog Wir haben uns 2008 mit Liquid Software auf den Weg gemacht, um die Art und Weise zu verändern, wie Unternehmen Software-Updates verwalten und veröffentlichen. Die Welt erwartet, dass Software fortlaufend, sicher, unaufdringlich und ohne Benutzereingriff aktualisiert wird. Diese hyperverbundene Erfahrung kann nur durch Automatisierung mit einer End-to-End-DevOps-Plattform und einem binärzentrierten Fokus ermöglicht werden.   Passende Artikel zum Thema   Read the full article

[govindhtech]

Intel Neural Compressor Joins ONNX in Open Source for AI

Intel Neural Compressor

In addition to popular model compression techniques like quantization, distillation, pruning (sparsity), and neural architecture search on popular frameworks like TensorFlow, PyTorch, ONNX Runtime, and MXNet, Intel Neural Compressor also aims to provide Intel extensions like Intel Extension for the PyTorch and Intel Extension for TensorFlow. Specifically, the tool offers the following main functions, common examples, and open collaborations:

Limited testing is done for AMD, ARM, and NVidia GPUs via ONNX Runtime; substantial testing is done for a wide range of Intel hardware, including Intel Xeon Scalable Processors, Intel Xeon CPU Max Series, Intel Data Centre GPU Flex Series, and Intel Data Centre GPU Max Series.

Utilising zero-code optimisation solutions, validate well-known LLMs like LLama2, Falcon, GPT-J, Bloom, and OPT as well as over 10,000 wide models like ResNet50, BERT-Large, and Stable Diffusion from well-known model hubs like Hugging Face, Torch Vision, and ONNX Model Zoo. Automatic accuracy-driven quantization techniques and neural coding.

Work together with open AI ecosystems like Hugging Face, PyTorch, ONNX, ONNX Runtime, and Lightning AI; cloud marketplaces like Google Cloud Platform, Amazon Web Services, and Azure; software platforms like Alibaba Cloud, Tencent TACO, and Microsoft Olive.

AI models

AI-enhanced apps will be the standard in the era of the AI PC, and developers are gradually substituting AI models for conventional code fragments. This rapidly developing trend is opening up new and fascinating user experiences, improving productivity, giving creators new tools, and facilitating fluid and organic collaboration experiences.

With the combination of CPU, GPU (Graphics Processing Unit), and NPU (Neural Processing Unit), AI PCs are offering the fundamental computing blocks to enable various AI experiences in order to meet the computing need for these models. But in order to give users the best possible experience with AI PCs and all of these computational engines, developers must condense these AI models, which is a difficult task. With the aim of addressing this issue, Intel is pleased to declare that it has embraced the open-source community and released the Neural Compressor tool under the ONNX project.

ONNX

An open ecosystem called Open Neural Network Exchange (ONNX) gives AI developers the freedom to select the appropriate tools as their projects advance. An open source format for AI models both deep learning and conventional ML is offered by ONNX. It provides definitions for standard data types, built-in operators, and an extendable computation graph model. At the moment, Intel concentrates on the skills required for inferencing, or scoring.

Widely supported, ONNX is present in a variety of hardware, tools, and frameworks. Facilitating communication between disparate frameworks and optimising the process from experimentation to manufacturing contribute to the AI community’s increased rate of invention. Intel extends an invitation to the community to work with us to advance ONNX.

How does a Neural Compressor Work?

With the help of Intel Neural Compressor, Neural Compressor seeks to offer widely used model compression approaches. Designed to optimise neural network models described in the Open Neural Network Exchange (ONNX) standard, it is a straightforward yet intelligent tool. ONNX models, the industry-leading open standard for AI model representation, enable smooth interchange across many platforms and frameworks. Now, Intel elevates ONNX to a new level with the Neural Compressor.

Neural Compressor

With a focus on ONNX model quantization, Neural Compressor seeks to offer well-liked model compression approaches including SmoothQuant and weight-only quantization via ONNX Runtime, which it inherits from Intel Neural Compressor. Specifically, the tool offers the following main functions, common examples, and open collaborations:

Support a large variety of Intel hardware, including AIPC and Intel Xeon Scalable Processors.

Utilising automatic accuracy-driven quantization techniques, validate well-known LLMs like LLama2 and wide models like BERT-base and ResNet50 from well-known model hubs like Hugging Face and ONNX Model Zoo.

Work together with open AI ecosystems Hugging Face, ONNX, and ONNX Runtime, as well as software platforms like Microsoft Olive.

Why Is It Important?

Efficiency grows increasingly important as AI begins to seep into people’s daily lives. Making the most of your hardware resources is essential whether you’re developing computer vision apps, natural language processors, or recommendation engines. How does the Neural Compressor accomplish this?

Minimising Model Footprint

Smaller models translate into quicker deployment, lower memory usage, and faster inference times. These qualities are essential for maintaining performance when executing your AI-powered application on the AI PC. Smaller models result in lower latency, greater throughput, and less data transfer all of which save money in server and cloud environments.

Quicker Inference

The Neural Compressor quantizes parameters, eliminates superfluous connections, and optimises model weights. With AI acceleration features like those built into Intel Core Ultra CPUs (Intel DLBoost), GPUs (Intel XMX), and NPUs (Intel AI Boost), this leads to lightning-fast inference.

AI PC Developer Benefits

Quicker Prototyping

Model compression and quantization are challenging! Through developer-friendly APIs, Neural Compressor enables developers to swiftly iterate on model architectures and effortlessly use cutting-edge quantization approaches such as 4-bit weight-only quantization and SmoothQuant.

Better User Experience

Your AI-driven apps will react quickly and please consumers with smooth interactions.

Simple deployment using models that comply with ONNX, providing native Windows API support for deployment on CPU, GPU, and NPU right out of the box.

What Comes Next?

Intel Neural Compressor Github

Intel looks forward to working with the developer community as part of the ONNX initiative and enhancing synergies in the ONNX ecosystem.

Read more on Govindhtech.com

[h1p3rn0v4]

“Si bien todos los sectores están en riesgo, el sector de los servicios financieros ha sido un blanco frecuente de ataques debido a la sensibilidad de los datos y las transacciones que maneja. En estos casos, un ataque de phishing exitoso puede tener consecuencias devastadoras en el mundo real para las víctimas".

[ericvanderburg]

Microsoft seized 240 sites used by the ONNX phishing service

http://i.securitythinkingcap.com/TGLXqt

[chess-engines-diary]

First official release of Ceres, including several major enhancements:

support for Ceres neural networks

full support of Chess960 (also known as Fischer Random) and DFRC (Double Fischer Random Chess) with the "UCI_Chess960" option for mode selection (contribution by lepned)

support of ONNX neural networks via CUDA or TensorRT execution providers for Ceres and Lc0 networks

[ai-news]

There is a growing demand for embedding models that balance accuracy, efficiency, and versatility. Existing models often struggle to achieve this balance, especially in scenarios ranging from low-resource applications to large-scale deployments. The #AI #ML #Automation

[forlinx]

Four Advantages Detailed Analysis of Forlinx Embedded FET3576-C System on Module

In order to fully meet the growing demand in the AIoT market for high-performance, high-computing-power, and low-power main controllers, Forlinx Embedded has recently launched the FET3576-C System on Module, designed based on the Rockchip RK3576 processor. It features excellent image and video processing capabilities, a rich array of interfaces and expansion options, low power consumption, and a wide range of application scenarios. This article delves into the distinctive benefits of the Forlinx Embedded FET3576-C SoM from four key aspects.

Advantages: 6TOPS computing power NPU, enabling AI applications

Forlinx Embedded FET3576-C SoM has a built-in 6TOPS super arithmetic NPU with excellent deep learning processing capability. It supports INT4/ INT8/ INT16/ FP16/ BF16/ TF32 operation. It supports dual-core working together or independently so that it can flexibly allocate computational resources according to the needs when dealing with complex deep learning tasks. It can also maintain high efficiency and stability when dealing with multiple deep-learning tasks.

FET3576-C SoM also supports TensorFlow, Caffe, Tflite, Pytorch, Onnx NN, Android NN and other deep learning frameworks. Developers can easily deploy existing deep learning models to the SoM and conduct rapid development and optimization. This broad compatibility not only lowers the development threshold, but also accelerates the promotion and adoption of deep learning applications.

Advantages: Firewall achieves true hardware resource isolation

The FET3576-C SoM with RK3576 processor supports RK Firewall technology, ensuring hardware resource isolation for access management between host devices, peripherals, and memory areas.

Access Control Policy - RK Firewall allows configuring policies to control which devices or system components access hardware resources. It includes IP address filtering, port control, and specific application access permissions. Combined with the AMP system, it efficiently manages access policies for diverse systems.

Hardware Resource Mapping and Monitoring - RK Firewall maps the hardware resources in the system, including memory areas, I/O devices, and network interfaces. By monitoring access to these resources, RK Firewall can track in real-time which devices or components are attempting to access specific resources.

Access Control Decision - When a device or component attempts to access hardware resources, RK Firewall will evaluate the access against predefined access control policies. If the access request complies with the policy requirements, access will be granted; otherwise, it will be denied.

Isolation Enforcement - For hardware resources identified as requiring isolation, RK Firewall will implement isolation measures to ensure that they can only be accessed by authorized devices or components.

In summary, RK Firewall achieves effective isolation and management of hardware resources by setting access control policies, monitoring hardware resource access, performing permission checks, and implementing isolation measures. These measures not only enhance system security but also ensure system stability and reliability.

Advantages: Ultra clear display + AI intelligent repair

With its powerful multimedia processing capability, FET3576-C SoM provides users with excellent visual experience. It supports H.264/H.265 codecs for smooth HD video playback in various scenarios, while offering five display interfaces (HDMI/eDP, MIPI DSI, Parallel, EBC, DP) to ensure compatibility with diverse devices.

FET3576-C SoM notably supports triple-screen display functionality, enabling simultaneous display of different content on three screens, significantly enhancing multitasking efficiency.

In addition, its 4K @ 120Hz ultra-clear display and super-resolution function not only brings excellent picture quality enjoyment, but also intelligently repairs blurred images, improves video frame rate, and brings users a clearer and smoother visual experience.

Advantage: FlexBus new parallel bus interface

FET3576-C of Forlinx Embedded offers a wide range of connectivity and transmission options with its excellent interface design and flexible parallel bus technology. The FlexBus interface on the SoM is particularly noteworthy due to its high flexibility and scalability, allowing it to emulate irregular or standard protocols to accommodate a variety of complex communication needs.

FlexBus supports parallel transmission of 2/4/8/16bits of data, enabling a significant increase in the data transfer rate, while the clock frequency of up to 100MHz further ensures the high efficiency and stability of data transmission.

In addition to the FlexBus interface, the FET3576-C SoM integrates a variety of bus transfer interfaces, including DSMC, CAN-FD, PCIe2.1, SATA3.0, USB3.2, SAI, I2C, I3C and UART. These interfaces not only enriches the SoM's application scenarios but also enhances its compatibility with other devices and systems.

It is easy to see that with the excellent advantages of high computing power NPU, RK Firewall, powerful multimedia processing capability and FlexBus interface, Forlinx Embedded FET3576-C SoM will become a strong player in the field of embedded hardware. Whether you are developing edge AI applications or in pursuit of high-performance, high-quality hardware devices, the Folinx Embedded FET3576-C SoM is an unmissable choice for you.

Originally published at www.forlinx.net.