AI & Machine Learning

  • Blending DSP and ML Features in a Low-power, General-Purpose Processor: How far can we go?

    Arm has been working on technologies that boost the signal processing and machine learning capabilities in the Arm Cortex-M55 processor.

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  • AIoT and the Future Data Storage

    One of the major factors of digital transformation is AIoT, which delivers intelligent connected systems that are capable of self-correcting.

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  • Aveva Releases Operations Management Interface (OMI) App

    Aveva Releases Operations Management Interface (OMI) App

    Aveva announced the release of its Aveva Insights Operations Management Interface (OMI) app. The application was designed to combine AI with an operator’s real-time decision-making.

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  • NXP Integrates Facebook’s Glow Neural Network Compiler into eIQ ML Software Development Framework

    NXP Integrates Facebook’s Glow Neural Network Compiler into eIQ ML Software Development Framework

    NXP combined the target-specific optimization capability of the Glow compiler with Arm Cortex-M and Cadence Tensilica HiFi 4 DSP neural network operator libraries within the eIQ IDE.

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  • Building, Training AI models Needn’t Be Confusing and Time Consuming

    Building, Training AI models Needn’t Be Confusing and Time Consuming

    As the technology gets pushed out to the Edge of the IoT, the number of uses climbs considerably. Developers are moving quickly toward deployment of their AI architectures.

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  • Eta Compute’s Tensai Flow Puts Machine Learning at the Edge of the IoT

    Eta Compute’s Tensai Flow Puts Machine Learning at the Edge of the IoT

    Deploying artificial intelligence and machine learning at the Edge of the IoT has long been the Holy Grail for design engineers.

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  • Dialog Semiconductor Announces Compatibility of EcoXiP Octal xSPI Flash Memory with Renesas’ RZ/A2M Microprocessor

    Dialog Semiconductor Announces Compatibility of EcoXiP Octal xSPI Flash Memory with Renesas’ RZ/A2M Microprocessor

    Octal flash device incorporating leading embedded AI processing with Renesas’ Dynamically Reconfigurable Processor (DRP) Technology targeted at Industrial IoT market.

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  • Architecture Exploration of AI/ML Applications and Processors

    Architecture Exploration of AI/ML Applications and Processors

    Architecture exploration of AI applications is complex and involves multiple studies. To start with, we can target a single problem such as memory access or can look at the full processor or system.

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  • Green Buildings Get a Boost: Wireless Sensor Nodes as a Key Application for Energy Harvesting

    Green Buildings Get a Boost: Wireless Sensor Nodes as a Key Application for Energy Harvesting

    The concept of energy harvesting has been around for over a decade; however, the implementation of ambient energy-powered systems in the real-world environment has been cumbersome, complex and costly.

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  • Artificial Intelligence and Machine Learning – Path to Intelligent Automation

    Artificial Intelligence and Machine Learning – Path to Intelligent Automation

    AI includes a wide range of technologies such as machine learning, deep learning (DL), optical character recognition (OCR), natural language processing (NLP), voice recognition, and so on.

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  • Scaling AIoT Projects Through Correct Component Selection

    Scaling AIoT Projects Through Correct Component Selection

    AIoT is the combined effort of the two technologies that have been dominating the field over the past few years (AI and IoT).

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  • AI Model Training & Execution Now Possible on MCUs via ONE Tech MicroAI Atom

    ONE Tech estimates that MicroAI Atom algorithms, which run recursive analysis and reside directly on target MCUs at the edge, reduce the cost of deploying endpoint intelligence by a minimum of 80%.

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  • Blending DSP and ML features into a low-power general-purpose processor – how far can we go?

    Blending DSP and ML features into a low-power general-purpose processor – how far can we go?

    New technology from Arm is blending DSP and ML features into a low-power general-purpose processor for the first time. What are the challenges? How far can we go?

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  • PFU Joins Qualcomm Smart Cities Accelerator Program and Collaborates with Innominds on Contactless Facial Biometric Tech

    PFU Joins Qualcomm Smart Cities Accelerator Program and Collaborates with Innominds on Contactless Facial Biometric Tech

    The collaboration with Innominds is expected to drive transformational initiatives in identity management through the combination of facial recognition technologies.

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  • AAEON Releases the BOXER-8251AI AI Edge Box PC

    AAEON Releases the BOXER-8251AI AI Edge Box PC

    AAEON announced the release of the BOXER-8251AI AI edge box PC powered by NVIDIA® Jetson Xavier™ NX.

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  • ICARVISIONS Releases IVMS(web) Platform for AI, Big Data

    ICARVISIONS Releases IVMS(web) Platform for AI, Big Data

    ICARVISIONS released its IVMS(web) platform for support of AI and big data.

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  • dSPACE Acquires Intempora, Strengthens Autonomous Driving Portfolio

    dSPACE Acquires Intempora, Strengthens Autonomous Driving Portfolio

    dSPACE announced it has acquired Intempora, a real-time development software company. The acquisition supports the strengthening of dSPACE’s autonomous driving product portfolio.

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  • How the SMARC™ Module 2.1 revision responds to the latest IoT- and AI-driven demands on embedded computing solutions

    How the SMARC™ Module 2.1 revision responds to the latest IoT- and AI-driven demands on embedded computing solutions

    Learn more about the new SMARC™ 2.1 specification, new key features and changes. Get a detailed comparison between the SMARC™ 2.0 module and the SMARC™ 2.1 module and find out more about special use..

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  • How Six Companies Are Using AI to Accelerate Automotive Technology

    How Six Companies Are Using AI to Accelerate Automotive Technology

    AI is not only used for path planning and obstacle avoidance, but incorporated in every step of development from modeling how systems will perform on the road to gathering parts for manufacturing.

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  • IoT and the Time-Critical Edge

    IoT and the Time-Critical Edge

    Challenges faced by IoT deployments related to latency, network bandwidth, reliability, and security cannot be addressed by cloud-only models, so the focus of IoT is moving towards the edge.

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