Posted on - 9 Comments on Banana Pi BPI-M4 Board Comes with RTD1395 Processor, M.2 Socket, Raspberry Pi 3B+ Form Factor

Banana Pi BPI-M4 Board Comes with RTD1395 Processor, M.2 Socket, Raspberry Pi 3B+ Form Factor

Banana Pi BPI-M4

Realtek RTD1395 processor is a lower cost version of RTD1295 / RTD1296 processors found in mid-range TV boxes often including HDMI input and output such as Zidoo X9s or LAKE I Home Cloud. The new processor comes with a lower-end Mali-470MP4 GPU and removed some interfaces like HDMI input and native SATA. So far, we had seen very few hardware platforms built around the processor, but SinoVoIP has just unveiled their latest Banana Pi BPI-M4 board powered by Realtek RTD1395 processor, following Raspberry Pi 3B+ form factor, and adding an M.2 key E socket with PCIe 2.0 and USB 2.0 signals. Banana Pi BPI-M4 specifications: SoC – Realtek RTD1395 quad-core Arm Cortex-A53 processor with Mali-470 MP4 GPU System Memory – 1 GB DDR4 (option 2 GB) Storage – 8G eMMC flash (max 64 GB), micro SD slot up to 256GB Video Output – 1x HDMI 2.0 port up to 1080p (TBC) Audio – Via HDMI port, 3.5mm audio jack Connectivity …

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Posted on - 126 Comments on ODROID-N2 Amlogic S922X SBC Coming in April for $63 and Up

ODROID-N2 Amlogic S922X SBC Coming in April for $63 and Up

ODROID N2

Due to RAM supply issues, Hardkernel canceled RK3399 based ODROID-N1 board last year, and decided to replace it with ODROID-N2 using a “newer SoC .. with faster CPU/GPU cores and native DDR4 support”, but they did not provide any details about the processor, and we speculated it could be the upcoming Amlogic S922X processor. Hardkernel has now formally unveiled ODROID-N2, the first Amlogic S922X SBC to be announced, with 2 to 4GB DDR4 RAM, 4x USB 3.0 ports, Gigabit Ethernet, HDMI 2.0a video output up to 4K 60p and more. ODROID-N2 SBC specifications: SoC – Amlogic S922X hexa-core big.LITTLE processor with 4x Arm Cortex A73 cores @ up to 1.8 GHz, 2x Arm Cortex A53 cores @ 1.9 GHz, Arm Mali-G52 GPU @ 846MHz; 12nm manufacturing process System Memory – 2GB or 4GB DDR4 RAM @ 1320 MHz Storage – 8MB SPI flash, eMMC flash module socket, micro SD card slot Video & Audio Output – HDMI up to 4K …

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Posted on - 28 Comments on Ubuntu 18.04 Now Boots on Some Snapdragon 835 Arm Laptops

Ubuntu 18.04 Now Boots on Some Snapdragon 835 Arm Laptops

Snapdragon 835 Ubuntu Arm Laptop

The first “proper” Windows 10 Arm laptops were unveiled at the end of 2017 and beginning of 2018, all based on Qualcomm Snapdragon 835 processor with always-on LTE connectivity, 20+ hour battery life, a fairly expensive price tag, and somewhat underwhelming performance. Qualcomm was not interested in supporting Linux, but there was interest from the community, and now it seems Ubuntu 18.04 images are available for Lenovo Miix 630, HP Envy x2, and ASUS Novago TP370 thanks to Aarch64-laptop project currently hosted on Github. Now the prebuilt images are not really ready for end users since UFS storage and WiFi are not working on any laptop yet, the touchpad is not working on the ASUS laptop, and accelerated graphics needs to be implemented. Interestingly WiFi is related to UFS on those laptops, and Marc Gonzalez is said to be being actively worked on UFS upstream support, which should enable for internal storage and WiFi. That means now you’d need to …

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Posted on - 2 Comments on Nitrogen8M_Mini is the First NXP i.MX8 8M Mini SBC

Nitrogen8M_Mini is the First NXP i.MX8 8M Mini SBC

Having just written about one of the first NXP i.MX 8M Mini systems-on-module, let’s stay with NXP’s latest 14-nm processor as Boundary Devices have been working on a variant of their Nitrogen8M SBC based on MXP i.MX 8M Mini processor and aptly called Nitrogen8M_Mini. The board is also known as the less human-friendly Nit8MQ_Mini_2r8e SBC, and comes with the following specifications: SoC – NXP i.MX 8M Mini with 4x Cortex A53 cores, 1x Cortex-M4F real-time core, Vivante GCNanoUltra 3D GPU, Vivante GC320 2D GPU System Memory –  2GB LPDDR4 (Optional 4GB version) Storage – 8GB eMMC flash, expandable up to 128GB, UHS SD card slot Display – 4-lane MIPI DSI interface up to 1080p Video Encode / Decode – 1080p H.264, VP8/1080p60 H.265, H.264, VP8, VP9 Audio – 3.5mm headphone jack, analog MIC jack, 2W audio amplifier, L&R speaker headers Camera Interface – 1x 4-lane MIPI-CSI interface Connectivity Gigabit Ethernet (RJ-45) Optional QCA9377 BD-SDMAC 802.11 ac version + Bluetooth 4.1 …

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Posted on - 3 Comments on F&S Elektronik PicoCore MX8MM System-on-Module Features NXP i.MX 8M Mini Processor

F&S Elektronik PicoCore MX8MM System-on-Module Features NXP i.MX 8M Mini Processor

FS i.MX 8M Mini SoM

Embedded World 2019 will take place at the end of this month on February 26-28 in Nuremberg, Germany, and we can expect plenty of news related to embedded systems. It’s very likely NXP i.MX 8M Mini processor will officially be launched, and we’ll see several modules and boards announced with the lower cost 14-nm processor. F&S Elektronik Systeme has already been promoting their upcoming PicoCore MX8MM system-on-module based on NXP i.MX 8M in their newsletter ahead of Embedded World, where they’ll showcase their solutions at booth 621 in hall 2. PicoCore MX8MM specifications: SoC – NXP i.MX 8M Mini single, dual, quad core Arm Cortex-A53 @ 1.8 GHz processor, Cortex-M4 real-time core @ 400 MHz, 512KB L2-Cache, 2D/3D GPU System Memory – Up to 8GB LPDDR4 Storage – Up to 512MB SLC NAND flash or up to 32GB eMMC flash Video Decode – 1080p60 HEVC H.265, VP9, H.264, VP8 Connectivity 802.11ac + Bluetooth 5.0 module Atheros AR8035 Gigabit Ethernet transceiver Interfaces …

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Posted on - 5 Comments on Autoware is an “All-in-One” Open-source Software for Autonomous Driving

Autoware is an “All-in-One” Open-source Software for Autonomous Driving

Autoware.AI Software Architecture

All major automotive companies, and some technology companies are all working on autonomous driving with the ultimate goal of achieving level 5 autonomous driving meaning no human intervention is needed at any stage. Development will take some more time, and companies are now competing with closed source software and hardware. But as I browsed through Linaro Connect Bangkok 2019 schedule, I found out there’s an open source autonomous driving software called Autoware.AI. Several “Autoware” projects are managed by the newly founded Autoware Foundation, a non-profit organization created to develop a synergy between corporate development and academic research in order to provide access to autonomous driving technology for everyone: Autoware.AI is the first version built on ROS 1, and Linux, and has been developed as a research and development platform Autoware.auto is the second version built on ROS 2, and Linux, with a complete redesign. Autoware.IO is an interface project for Autoware to be extended with proprietary software and third-party libraries for …

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Posted on - 6 Comments on Adding Machine Learning based Image Processing to your Embedded Product

Adding Machine Learning based Image Processing to your Embedded Product

Convert model tensorflow runtime to NNEF

CNXSoft: This is a guest post by Greg Lytle, V.P. Engineering, Au-Zone Technologies. Au-Zone Technologies is part of the Toradex Partner Network. Object detection and classification on a low-power Arm SoC Machine learning techniques have proven to be very effective for a wide range of image processing and classification tasks. While many embedded IoT systems deployed to date have leveraged connected cloud-based resources for machine learning, there is a growing trend to implement this processing at the edge. Selecting the appropriate system components and tools to implement this image processing at the edge lowers the effort, time, and risk of these designs. This is illustrated with an example implementation that detects and classifies different pasta types on a moving conveyor belt. Example Use Case For this example, we will consider the problem of detecting and classifying different objects on a conveyor belt. We have selected commercial pasta as an example but this general technique can be applied to most other …

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Posted on - 34 Comments on Qihua CQH6 SoM – A Better Alternative to Raspberry Pi Compute Model 3+?

Qihua CQH6 SoM – A Better Alternative to Raspberry Pi Compute Model 3+?

Allwinner H6 SoM

The Raspberry Pi Foundation just launched the Raspberry Pi Compute Module 3+ for $25 and up with Broadcom BCM2837B0 processor offering better thermals than BCM2837 processor. As is often the case, this post generated some insightful comments from the smart readers of CNX Software, and they pointed out some of the shortcomings of the module such as the lack of Ethernet support that would be required for cluster use cases for example. You can also add a USB to Ethernet chip to the carrier board, but that’s not ideal, and instead someone linked to Qihua CQH6 system-on-module powered by Allwinner H6 processor and sold for 158 RMB ($23.5 USD) or 199 RMB ($29.65) for respectively 512MB RAM/4GB flash and 1GB RAM/8GB flash configurations. Qihua CQH6 module specifications: SoC – Allwinner H6 quad-core Cortex A53 processor @ 1.8 GHz with Arm Mali-T720MP GPU System Memory – 512MB, 1GB or 2GB DDR3L RAM Storage – 4GB, 8GB, 16GB, or 32GB eMMC 5.1 …

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