Low power dual-channel receive modules and pre-drivers boost 5G, says NXP

Pre-drivers and dual-channel receive front end modules from NXP Semiconductors are claimed to reduce 5G operating costs. The BTS6302U/6201U pre-drivers and BTS7203/5 dual-channel receive (RX) front end modules are intended for 5G massive multi-input multi-output (MIMO) infrastructure. They are claimed to offer low current consumption, reducing carrier operating costs. They have been developed using NXP’s silicon germanium (SiGe) process and in-house test and assembly.

Traditional basestations provide four to eight transmit and receive channels, but the 5G MIMO infrastructure designs typically require 32 or 64 transmit and receive channels to deliver the increased network throughput and responsiveness of 5G. The dual-channel RX FEMs address the need for additional channels, while simultaneously reducing power needs and operating costs for both carriers and OEMs, says NXP.

The BTS6302U pre-driver offers optimised 5G performance, with low current consumption and an integrated balun to reduce external components, simplifying design and reducing overall system costs. The pre-driver and RX FEMs are compatible with NXP’s RapidRF series of reference boards, to further reduce 5G development cycles and time to market.

NXP Semiconductors specialises in secure connectivity solutions for embedded applications. NXP says it is driving innovation in the automotive, industrial and IoT, mobile, and communication infrastructure markets. 

http://www.nxp.com

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Sensor development kit delivers AI/ML for smart industrial applications

AI tool developer, SensiML has teamed up with onsemi to deliver ML for autonomous sensor data processing and predictive modelling. SensiML develops AI tools for building intelligent IoT endpoints. Its analytics toolkit development software has been combined with the RSL10 sensor development kit from onsemi for edge sensing applications such as industrial process control and monitoring. SensiML can support AI functions in a small memory footprint, and the RSL10 provides advanced sensing and Bluetooth Low Energy connectivity for smart sensing without the need for cloud analytics of highly dynamic raw sensor data.

Claimed to have the industry’s lowest power Bluetooth Low Energy connectivity,  the RSL10 sensor development kit combines the RSL10 radio with a range of environmental and inertial motion sensors onto a tiny form factor board that interfaces with the SensiML Toolkit. Developers using the RSL10-based platform and the SensiML software together can add low latency local AI predictive algorithms to industrial wearables, robotics, process control, or predictive maintenance applications regardless of expertise in data science and AI. 

The auto-generated code enables smart sensing embedded endpoints that transform raw sensor data into critical insight events where they occur and can take appropriate action in real time. The smart endpoints reduce network traffic by communicating data only when it offers valuable insight.

“Cloud-based analytics add unwanted, non-deterministic latency, and are too slow, too remote and too unreliable for critical industrial processes,” said Dave Priscak, vice president of Applications Engineering at onsemi. 

“Other AutoML solutions for the edge rely only on neural network classification models with only rudimentary AutoML provisions, yielding suboptimal code for a given application,” adds Chris Rogers, SensiML’s CEO. The company’s AutoML model search includes neural networks with an array of classic ML algorithms, as well as segmenters, feature selection, and digital signal conditioning transforms, he explains.

The SensiML analytics toolkit is available now from SensiML and the RSL10 sensor development kit is available now from onsemi.

 http://www.sensiml.com

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IMG CXT is “most advanced ray tracing GPU” says Imagination

With the capability to bring desktop-quality visuals to mobile applications, Imagination Technologies announces the IMG CXT GPU IP which uses Imagination’s PowerVR Photon ray tracing architecture. It has up to 50 per cent more compute and geometry performance per core compared to BXT.

By adding Photon hardware ray tracing, the IMG CXT advances GPU IP, says Imagination, delivering performance for games and other graphical use cases. The Photon ray tracing architecture brings desktop-quality visuals to mobile and embedded applications, says the company. It imitates how light behaves in the real world, making it possible to create 3D scenes that are “near indistinguishable from real life”. Ray tracing is relatively recent to desktop PCs and gaming consoles, but IMG CXT’s Photon architecture allows mobile developers and gamers to take advantage of its rendering technology with full hardware acceleration. Photon brings ray tracing to mobile, gaming, AR, desktop, data centre, cloud and automotive designs.

Imagination has identified six levels of ray tracing (from Level 0 to Level 5). The Photon architecture inside IMG CXT is Level 4, enhancing ray tracing performance and efficiency to deliver a desktop-quality experience for mobile gamers and developers.

The IMG CXT-48-1536 RT3 core features three instances of the ray acceleration cluster (RAC), offering up to a total of 1.3Gray per second. This delivers photorealistic ray traced shadows, reflections, global illumination, and ambient occlusion, with high frame rates, in a mobile power budget, says Imagination.

For rasterised graphics performance, IMG CXT has 50 per cent more compute, texturing and geometry performance than Imagination’s previous GPU IP. Its low power superscalar architecture delivers high performance at low clock frequencies for exceptional FPS/W efficiency, while Imagination Image Compression (IMGIC) reduces bandwidth requirements.

The Photon architecture can be scaled to cloud, data centre and PC markets using Imagination’s multi-core technology. This can generate up to 9TFLOPS of FP32 rasterised performance and over 7.8Gray per second of ray tracing performance, offering up to 2.5 times greater power efficiency than Level 2 or 3 RTLS ray tracing.

IMG CXT can also be used for automotive human machine interface (HMI) platforms. It enables the photo-realistic representation of vehicles for surround view and dense urban areas for sat-navs, as well as mixed-reality heads up displays (HUDs), digital speedometers and other information displays. It can also deliver premium in-car gaming and entertainment experiences while occupants wait for the vehicle to charge.

The PowerVR Photon architecture features the RAC, a low power, dedicated hardware GPU block which accelerates and offloads more of the ray tracing computations from the shader cores compared to less-efficient Level 2 RTLS architectures.

The RAC consists of the ray store, ray task scheduler and coherency gatherer and is closely coupled to two 128-wide unified shading clusters (USCs) featuring high-speed dedicated data paths for the most efficient and lowest power ray traced deployment. The ray store keeps ray data structures on-chip during processing, providing high-bandwidth read and write access to all units in the RAC, avoiding slowdowns or power increases from storing or reading ray data to DRAM. The ray task scheduler offloads the shader clusters, deploying and tracking ray workloads with dedicated hardware, keeping ray throughput high and power consumption low. The coherency gatherer unit analyses all rays in flight and bundles rays from across the scene into coherent groups enabling them to be processed with much greater efficiency.

These features combine to deliver greater and more consistent ray tracing performance for developers than current solutions on the market, says Imagination, for advanced effects when rendering complex surfaces such as cars, characters, and terrain, to create more detailed environments than previously possible.

http://www.imaginationtech.com

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Tool accelerates edge-native AI development

To simplify and accelerate the design, development, testing and deployment of smart systems, MicroAI has introduced Launchpad, a quick start development and deployment tool. It is designed to run embedded MicroAI software on microcontrollers  and microprocessors in edge and endpoint devices.

The tool handles customers with SIMs around the world and provides a flexible way to manage and reconfigure device profiles. Launchpad allows engineers to customise dashboards, including account creation, authentication, mobile SIM or LoRaWAN connectivity activation, credit card billing for global SIM connectivity with MicroAI’s embedded software libraries.

“MicroAI’s goal is to democratise the development of smart machines for all organisations across any industry,” said MicroAI CEO Yasser Khan. “Regardless of industry or product, building . . .  smart device includes creating an edge AI model, but also integrating connectivity and cloud resources, as well as device activation and management.”

MicroAI’s embedded software, AtomML, enables OEMs to deploy personalised, edge-native AI models, without needing to develop static edge-AI models first in a cloud or laptop and then port them to the embedded device. Instead, MicroAI AtomML moves the training and inferencing directly to the embedded device. Launchpad then simplifies and reduces the time and cost to integrate the microcontrollers and microprocessors into an edge device, which can be tested and scaled to proof of concept for mass deployment.

MicroAI Launchpad can be white labelled for use by semiconductor companies, OEMs  and service providers. Semiconductor companies that offer stock keeping units (SKUs) with MicroAI embedded AI software can leverage its end-to-end device management and deliver it to their customers to help expedite design, development, testing and deployment. OEMs directly engaged with MicroAI benefit from Launchpad’s flexibility to evaluate various hardware, software, and cloud solutions before finalising a deployment model. For IoT service providers, Launchpad will provide device certification, connectivity, and deployment for a deeper insight view of connected devices on networks.

MicroAI is based in Dallas, Texas, USA and specialises in edge-native artificial intelligence (AI) and machine learning (ML) products. The company is personalising AI for connected machines, edge devices, and critical assets by embedding its proprietary edge-native AI technology directly onto microcontrollers and microprocessors within edge endpoints. This enables device-specific and more accurate AI modelling for edge and endpoint cyber security, advanced predictive maintenance, IoT performance optimisation and significant improvements in overall equipment effectiveness (OEE). The company’s mission is to democratise edge-native AI for all connected, smart devices by reducing the complexity, time, and cost to design, develop, and deploy embedded, edge-native AI.

http://www.micro.ai

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