$1,000 Microsoft Surface Pro X Tablet is Powered by Microsoft SQ1 Arm Processor

Microsoft Surface Pro X

Arm Windows 10 laptops, tablets, and 2-in-1 hybrids have been around for a couple of years all powered by Qualcomm Snapdragon processors so far. Now Microsoft is launching Surface Pro X Windows 10 tablet powered by its own Microsoft SQ1 Arm processor. It sells for $999 with 8GB RAM, 128GB storage, and a 13″ 2880 x 1920 x pixel touchscreen display in the default configuration, but you can also customize your order with up to 16GB RAM,  512GB SSD storage, optional keyboard and stylus for over $2,000. Microsoft Surface Pro X Specification Microsoft Surface Pro X specification: SoC – Microsoft SQ1 Arm processor @ 3.0 GHz with Adreno 685 GPU System Memory – 8GB or 16GB LPDDR4x RAM at 3733Mbps (option for 16GB) Storage – Removable 128, 256, or 512GB M.2 SSD Display – 13” PixelSense Display with 2880 x 1920 resolution (3:2 aspect ratio), 10 point multi-touch, 450 nits brightness Audio –  Dual far-field Studio Mics; 2W stereo speakers …

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Microsoft Unveils The Vision AI Developer Kit For AI on the Edge

The field of Artificial Intelligence is getting more exciting every single day, and the big corporations and startups are massively pouring for it. One thing I am undoubtedly sure about is that the future will certainly be remarkable. Microsoft, the biggest software company in the world with sales over $100 billion has been venturing in the domain of artificial intelligence for a while now with cloud computing platform Azure and other related cloud computing services, but instead of its usual cloud computing route for AI, Microsoft is banking on AI on the edge with the introduction of the Vision AI Developer Kit in joint partnership with the semiconductor giant Qualcomm. With millions of data collected at the Edge, the potential of artificial intelligence on the edge is promising. AI cases performed on the Edge will help in making critical decisions, and more data insight can even facilitate important business scenarios. Microsoft, in partnership with Qualcomm, announced a developer kit back …

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Microsoft to Support exFAT File System in Linux, Releases exFAT Specification

Linux Kernel exFAT

Microsoft’s exFAT file system is quite popular for removable mass storage devices such as SD cards and USB flash drives as it’s supported in Windows, and many consumers devices such as cameras can handle Microsoft’s patented file system. The “patent” part causes an issue in Linux, as companies need to license it in order to ship it in their products or operating systems image. I recently re-installed Ubuntu 18.04 on my laptop, and if I reinsert my “test” USB drive: BTRFS, EXT-4, and NTFS partitions all mount automatically, but not the exFAT one. If I click on the partition, I get this message: That’s because Canonical does not provide exFAT by default in Ubuntu due to legal issues. It’s however easy enough for the user to install exFAT utilities The drive will mount successfully: Note that it’s using FUSE (Filesystem in Userspace), and it’s usually not a problem with today’s fast processors, but I remember adding NTFS via Fuse in …

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Espressif Rolls out ESP32 Boards for Microsoft Azure IoT & Google Cloud IoT Core Services

ESP32 Azure IoT Kit

Espressif ESP32 WiFi & Bluetooth processor is pretty versatile, and you could connect any ESP32 board to any cloud services with some efforts. But to make things even easier Espressif Systems worked with Microsoft and Google to release versions of ESP32 boards specifically designed to connect to Microsoft Azure IoT or Google Cloud IoT core. Meet ESP32-Azure IoT Kit and ESP32-DevKitC Google Cloud IoT. ESP32-Azure IoT Kit Hardware specifications: Wireless Module – ESP32-WROVER-B WiFi and Bluetooth module Storage – MicroSD card socket Display – 0.96” blue and yellow OLED display driven by SSD1306 I2C driver chip Sensors InvenSense MPU6050 motion sensor NXP MAG3110 magnetometer FBM320 barometer STMicro HTS221 humidity & temperature sensor ROHM BH1750FVI light sensor Expansion – 16-pin header Debugging – USB to UART bridge for serial debugging & programming Misc – Reset button, user button, 2x charge LED’s, 2x user LED’s, 1x passive buzzer Power Supply – 5V via micro USB port; 3-pin header for LiPo battery; charging …

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Windows Subsystem for Linux 2 Gets a Linux Kernel, Faster File System, Docker Support

Windows Subsystem for Linux 2 WSL 2

Microsoft first introduced Windows Subsystem for Linux in 2016 in order to let developers runs bash command from Ubuntu user space without having to install Ubuntu in a virtual machine or container. It relies on the Windows kernel with a library converting Linux system calls into ones compatible with Windows. Performance is great until you start to involve file systems calls, for example during code compilations, something that’s fairly common for developers… Microsoft has been working on solving this performance issue, and compatibility issues with software such as Docker, and is now close to releasing Windows Subsystem for Linux 2 (WSL 2) featuring its own Linux 4.19 kernel instead of the Windows kernel plus a translation layer. WSL 2 uses virtualization technology to run its custom Linux kernel inside of a lightweight utility virtual machine (VM) which takes just 2 seconds to boot. That also means there will be separate (security) updates for the Windows kernel and the Linux kernel …

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Avnet Azure Sphere MT3620 Starter Kit Features Two mikroBUS Sockets

Avnet Azure Sphere MT3620 Starter Kit

Microsoft and MediaTek worked together to design MediaTek MT3620 Arm Cortex-A7 processor with Microsoft Pluton security sub-system required for Microsoft Azure Sphere IoT ecosystem. We’ve already covered boards from Seeed Studio including the just announced low cost MT3620 mini dev board. But Microsoft also cooperated with Avnet which has recently introduced Azure Sphere MT3620 Starter Kit equipped with two mikroBUS sockets enabling the platform to leverage one of the 633 “click boards” available from MikroElektronika. Just like the latest Seeed Studio board, Avnet Azure Sphere MT3620 Starter Kit is comprised as a baseboard with a soldered-on CPU module that can later be used for mass-production is a custom designed board. Specifications: Azure Sphere MT3620 CPU Module Mediatek MT3620AN single core Arm Cortex-A7 processor @ 500 MHz with 4MB SRAM, dual core Arm Cortex-M4F real-time core @ 200 MHz with 64KB RAM, Microsoft Pluton security sub-system, and WiFi. Storage – TBD Connectivity –  Dual band 802.11 a/b/g/n WiFi with chip antenna …

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MT3620 Mini Dev Board is a Cheaper Microsoft Azure Sphere Board

MT3620 Mini Dev Board

Announced nearly one year ago, Microsoft Azure Sphere is an ecosystem comprised of Azure MCUs with Microsoft Pluton Security System, Linux based Azure Sphere OS, and a secure cloud service called Azure Sphere Security Service. The first official Azure development board – MT3620 Development Board for Azure Sphere – was launched last year for $84.95. The kit may not have attracted a large number of developers, so there’s now a cheaper version – MT3620 Mini Dev board – going for $34.90 on Seeed Studio. Note that’s a pre-order and shipping is scheduled for May 13, 2019. MT3620 Mini dev board specifications: CPU Module – AI-Link WF-M620-RSA1 module with Mediatek MT3620AN single core Arm Cortex-A7 processor @ 500 MHz with 4MB SRAM, dual core Arm Cortex-M4F real-time core @ 200 MHz with 64KB RAM, Pluton security sub-system, and WiFi. Storage – 2x 8MB dual channel quad SPI (TBC) Connectivity –  Dual band 802.11 a/b/g/n WiFi 1T1R with main and aux PCB antennas, …

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Bonsai Algorithm Enables Machine Learning on Arduino with a 2KB RAM Footprint

Bonsai Machine Learning

Machine learning used to be executed in the cloud, then the inference part  moved to the edge, and we’ve even seen micro-controllers able to do image recognition with GAP8 RISC-V micro-controller. But I’ve recently come across a white paper entitled “Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things” that shows how it’s possible to perform such tasks with very little resources. Here’s the abstract: This paper develops a novel tree-based algorithm, called Bonsai, for efficient prediction on IoT devices – such as those based on the Arduino Uno board having an 8 bit ATmega328P microcontroller operating at 16 MHz with no native floating point support, 2 KB RAM and 32 KB read-only flash. Bonsai maintains prediction accuracy while minimizing model size and prediction costs by: (a) developing a tree model which learns a single, shallow, sparse tree with powerful nodes; (b) sparsely projecting all data into a low-dimensional space in which the tree is learnt; and …

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