Linux 4.17 Release – Main Changes, Arm & MIPS Architectures

Linus Torvalds released Linux 4.17 last Sunday: So this last week was pretty calm, even if the pattern of most of the stuff coming in on a Friday made it feel less so as the weekend approached. And while I would have liked even less changes, I really didn’t get the feeling that another week would help the release in any way, so here we are, with 4.17 released. No, I didn’t call it 5.0, even though all the git object count numerology was in place for that. It will happen in the not _too_distant future, and I’m told all the release scripts on kernel.org are ready for it, but I didn’t feel there was any real reason for it. I suspect that around 4.20 – which is I run out of fingers and toes to keep track of minor releases, and thus start getting mightily confused – I’ll switch over. That was what happened for 4.0, after all. As […]

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Toradex Launches Apalis iMX8 Computer-on-Module based on NXP i.MX 8QuadMax SoC

Toradex-Apalis-i.MX8-SoM

Toradex Apalis iMX8 is another system-on-module powered by NXP i.MX 8QuadMax hexa core Arm Cortex A72 + 53 processor, which comes with 4GB LPDDR4 RAM, up to 16GB flash, and an on-board dual-band 802.11ac 2×2 MU-MIMO Wi-Fi and Bluetooth 5 ready module. The company has just opened early access for selected customers, so it has become possible to start developing products with the MXM3 computer-on-module. Apalix i.MX8 module specifications: SoC – NXP i.MX 8QuadMax hexa core processor with 2x Arm Cortex-A72 cores @ 1.6 GHz, 4x Arm Cortex-A53 cores @ 1.26 GHz, 2x Cortex-M4 real-time core @ 266 MHz, and dual Vivante GC7000XSVX GPU System Memory – 4GB LPDDR4 (64 Bit) Storage – Up to 16GB eMMC flash On-module Connectivity Gigabit Ethernet via Microchip KSZ9031 transceiver with low power features Dual-band 802.11ac 2×2 MU-MIMO Wi-Fi and Bluetooth 5 via Azurewave AW-CM276NF M.2 1216 LGA module Audio – NXP SGTL5000 low power stereo codec 314-pim MXM3 edge connector with: Storage – […]

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Gumstix Chatterbox is a Customizable, AVS-Ready Development Platform based on Toradex Colibri i.MX7 SoM

There are several official Amazon Voice Service (AVS) development kits, and we’ve already covered a few of them with the likes of Allwinner 3-Mic Far Field AVS Development Kit or Intel Speech Enabling Developer Kit, but you can also design your own board conformant to Amazon’s Alexa Voice Service functional design guide. That’s exactly what Gumstix has done with Chatterbox carrier board for Toradex Colibri i.MX7 system-on-module powered by NXP i.MX7 Arm Cortex A7 processor. Designed in Geppeto, the board includes a 2.5-Watt speaker driver, an on-board microphone, and Line IN and headphone jacks, as well as WiFi, Bluetooth and Ethernet for connectivity.   Gumstix Chatterbox specifications: Colibri iMX7 SO-DIMM socket Storage – microSD card slot Audio – On-board microphone; 2.5W speaker driver; Line-in & headphone jacks Connectivity – Gigabit Ethernet, 802.11 b/g/n WiFi & Bluetooth 4.1 LE (TI WiLink8 WL1831) with u.FL antenna connector USB – 1x micro USB port, 1x USB host port Debugging – 1x micro USB […]

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Gumstix AutoBSP Automatically Generates Device Tree Files for Hardware Designed with Geppetto

Gumstix launched Geppetto Design-To-Order (D2O) system back in 2013, and at the time you could design complete baseboard for their Overo CoMs right in your Chrome or Firefox web browser, and once complete, order the board from the website. The system is meant to save you time, and “design” here does not mean drawing schematics, and laying out PCBs, but instead selecting board size, and adding ports as needed. Since then, the company has added support for more modules, and you can now easily build you own baseboard for Raspberry Pi Compute Module, Technexion PICO-IMX6 module, Toradex Colibri SoM, 96Boards Mezzanine, and they even have Beaglebone Black and 96Board CE or IoT connectors, among others. Support for Qualcomm DragonBoard 410c, Atmel (Arduino) , and STMicro platforms is also being worked on. Their latest feature – AutoBSP – automatically generates device tree files for your custom boards, so you can simply copy it to your favorite image and get started as […]

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Getting Started with OpenCV for Tegra on NVIDIA Tegra K1, CPU vs GPU Computer Vision Comparison

This is a guest post by Leonardo Graboski Veiga, Field Application Engineer, Toradex Brasil Introduction Computer vision (CV) is everywhere – from cars to surveillance and production lines, the need for efficient, low power consumption yet powerful embedded systems is nowadays one of the bleeding edge scenarios of technology development. Since this is a very computationally intensive task, running computer vision algorithms in an embedded system CPU might not be enough for some applications. Developers and scientists have noticed that the use of dedicated hardware, such as co-processors and GPUs – the latter traditionally employed for graphics rendering – can greatly improve CV algorithms performance. In the embedded scenario, things usually are not as simple as they look. Embedded GPUs tend to be different from desktop GPUs, thus requiring many workarounds to get extra performance from them. A good example of a drawback from embedded GPUs is that they are hardly supported by OpenCV – the de facto standard libraries […]

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Christmann RECS|Box Atlas Quad Apalis Microserver Evaluation Kit Supports Four Toradex Apalis SoM

System-on-modules are normally used in low volume embedded systems, but they can also be used in microservers, for example to upgrade capacity as needed. Christmann informationstechnik + medien GmbH has developed a microserver evaluation kit taking up to 4 Toradex Apalis SoMs for example based on Nvidia Tegra K1 processor, and also offers full rack systems with up to 72 modules. Christmann RECS|Box Atlas Quad Apalis specifications: Modules – 4x Slots for Apalis SoM Connectivity – 1 GBit/s Compute Ethernet, 1 GBit/s Management Ethernet Video Output – 1x HDMI USB – 3x USB host ports, 1x micro USB port Misc – 5 Status LEDs for USB, communication, and serial console, 4x fan connectors, KWM switch, 5x temperature sensors, 6x current sensors, 1x voltage monitor, fan speed monitoring Power Supply – 12V via a 4-pin jack Dimensions – 300 x 145 x 68 mm The evaluation kit includes an Atlas board with an  acrylic base plate and 2 fans, a power […]

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A Look at Three Options to Develop Real-Time Linux Systems on Application Processors – HMP, Real-Time Linux and Xenomai

This is a guest post by written by Guilherme Fernandes, Raul Muñoz, Leonardo Veiga, Brandon Shibley, all working for Toradex. Introduction Application processor usage continues to broaden. System-on-Chips, usually powered by ARM Cortex-A cores, are taking over several spaces where small ARM Cortex-M, and other microcontroller devices, have traditionally dominated. This trend is driven by several facts, such as: The strong requirements for connectivity, often related to IoT and not only from a hardware point of view, but also related to software, protocols and security The need for highly interactive interfaces such as multi-touch, high resolution screens and elaborate graphical user interfaces; The decreasing price of SoCs, as consequence of its volume gain and new production capabilities. Typical cases exemplifying the statement above are the customers we see every day starting a product redesign upgrading from a microcontroller to a microprocessor. This move offers new challenges as the design is more complicated and the operating system abstraction layer is much […]

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Allwinner A64 based Pine A64 and Banana Pi M64 Boards Can Now Run Windows 10 IoT Core

Windows IoT is a version of Windows 10 that’s optimized for smaller devices with or without a display, and was fist released for Raspberry Pi 2 and MinnowBoard MAX. Since then a few more boards are now officially supported, including DragonBoard 410c, and Raspberry Pi 3. But there’s been some recent developments as two Allwinner A64 64-bit ARM boards are now supported according to two wiki entries (here and there) explaining how to run a simple Csharp sample on Windows 10 IoT Core on either Banana Pi M64 or Pine A64 boards. The guide shows how to configure Azure IoT Hub, register the IoT device, and build and deploy Azure IoT SDK on the board. But basically if all you want to is to run Windows IoT core on either board, you’ll need to download either: Windows 10 IoT Core for Banana Pi M64: Windows10IoT_BPI-M64.ffu (Link removed as Microsoft does not allow redistribution of ffu for now, despite the link […]

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