Microchip SAMD21 Machine Learning Evaluation Kits Work with Cartesiam, Edge Impulse and Motion Gestures Solutions

While it all started in the cloud Artificial Intelligence is now moving at the very edge is ultra-low power nodes, and Microchip has launched two SAMD21 Arm Cortex-M0+ machine learning evaluation kits that now work with AI/ML solutions from Cartesiam, Edge Impulse, and Motion Gestures. Bot machine learning evaluation kits come with SAMD21G18 Arm Cortex-M0+  32-bit MCU, an on-board debugger (nEDBG), an ATECC608A CryptoAuthentication secure element, ATWINC1510 Wi-Fi network controller, as well as Microchip MCP9808 high accuracy temperature sensor and a light sensor. But EV45Y33A development kit is equipped with an add-on board featuring Bosch’s BMI160 low-power Inertial Measurement Unit (IMU), while EV18H79A features an add-on board with TDK InvenSense ICM-42688-P  6-axis MEMS. The photo above makes it clear both SAMD21 machine learning evaluation kits rely on the same baseboard with a MikroBus socket connected to either 6DOF IMU 2click or 6DOF IMU 14 click add-on board from MikroElektronika. Both boards are similar, but the latter was just released this …

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CrowPi2 Raspberry Pi 4 Education Laptop Review

I started my review of CrowPi2 Raspberry Pi 4 Learning Kit a while ago and at the time I showed content from the kit and its first boot. I’ve now spent more time with this very special Raspberry Pi 4 laptop and will focus this review on the education part, namely CrowPi2 software, but will also look at thermal cooling under stress with and without a fan, and try to install another Raspberry Pi compatible board inside the laptop shell. CrowPi2 Education Software It’s quite important to read the user manual before getting started as there are a few non-intuitive steps you may have to take. First I assume the wireless keyboard would just connect after pressing the power button, but it did not. The user manual explains the RF dongle is inside the mouse, and once you connect it you’ll be able to use the keyboard that has some battery charge out of the box, and can be charged …

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Edge Impulse Enables Machine Learning on Cortex-M Embedded Devices

Artificial intelligence used to happen almost exclusively in the cloud, but this introduces delays (latency) for the users and higher costs for the provider, so it’s now very common to have on-device AI on mobile phones or other systems powered by application processors. But recently there’s been a push to bring machine learning capabilities to even lower-end embedded systems powered by microcontrollers, as we’ve seen with GAP8 RISC-V IoT processor or Arm Cortex-M55 core and the Ethos-U55 micro NPU for Cortex-M microcontrollers, as well as Tensorflow Lite. Edge Impulse is another solution that aims to ease deployment of machine learning applications on Cortex-M embedded devices (aka Embedded ML or TinyML) by collecting real-world sensor data, training ML models on this data in the cloud, and then deploying the model back to the embedded device. The company collaborated with Arduino and announced support for the Arduino Nano 33 BLE Sense and other 32-bit Arduino boards last May. The solution supports motion …

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ECM3532 AI Sensor Board Features Cortex-M3 MCU & 16-bit DSP “TENSAI” SoC for TinyML Applications

Eta Compute ECM3532 is a system-on-chip (SoC) with a Cortex-M3 microcontroller clocked at up to 100 Mhz, and NXP CoolFlux 16-bit DSP designed for machine learning on embedded devices, aka TinyML, and part of the company’s TENSAI platform. The chip is also integrated into the ECM3532 AI sensor board featuring two MEMS microphones, a pressure & temperature sensor, and a 6-axis motion sensor (accel/gyro) all powered by a CR2032 coin-cell battery. ECM3532 AI sensor board specifications: SoC – ECM3532 neural sensor processor with Arm Cortex-M3 core @ up to 100 MHz (< 5μA/MHz run mode) combines with 512KB embedded FLASH, 256KB SRAM, and 8KB BootROM + secure bootloader, and NXP CoolFlux 16-bit DSP @ up to 100 MHz with 32KB program memory, 64KB data memory. See the product brief for details. Storage – 64Mbit serial Flash for datalogging Connectivity – Bluetooth 4.2 LE via ABOV Semiconductor A31R118 and PCB antenna Sensors 2x PDM MEMS microphones (TDK-Invensense ICS-41350) Pressure/Temperature sensor (BOSCH …

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QuickFeather Board is Powered by QuickLogic EOS S3 Cortex-M4F MCU with embedded FPGA (Crowdfunding)

Yesterday, I wrote about what I felt what a pretty unique board: Evo M51 board following Adafruit Feather form factor, and equipped with an Atmel SAMD51 Cortex-M4F MCU and an Intel MAX 10 FPGA. But less than 24 hours later, I’ve come across another Adafruit Feather-sized Cortex-M4F board with FPGA fabric. But instead of using a two-chip solution, QuickLogic QuickFeather board leverages the company’s EOS S3 SoC with a low-power Cortex-M4F core and embedded FPGA fabric. QuickFeather specifications: SoC – QuickLogic EOS S3 with Arm Cortex-M4F Microcontroller @ up to 80 MHz and 512 Kb SRAM, plus an embedded FPGA (eFPGA) with 2400 effective logic cells and 64Kb RAM Storage – 16Mbit SPI NOR flash USB – Micro USB  port with data signals tied to eFPGA programmable logic Sensors – Accelerometer, pressure sensor, built-in PDM microphone Expansion I/Os – Breadboard-compatible 0.1″ (2.54 mm) pitch headers including 20 Feather-defined GPIO + 13 additional GPIO with UART, I2C, I2S and SPI Debugging …

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Bamboo Systems B1000N 1U Server Features up to 128 64-bit Arm Cores, 512GB RAM

SolidRun CEx7-LX2160A COM Express module with NXP LX2160A 16-core Arm Cortex A72 processor has been found in the company’s Janux GS31 Edge AI server in combination with several Gyrfalcon AI accelerators. But now another company – Bamboo Systems – has now launched its own servers based on up to eight CEx7-LX2160A module providing 128 Arm Cortex-A72 cores, support for up to 512GB DDR4 ECC, up to 64TB NVMe SSD storage, and delivering a maximum of 160Gb/s network bandwidth in a single rack unit. Bamboo Systems B1000N Server specifications: B1004N – 1 Blade System B1008N – 2 Blade System N series Blade with 4x compute nodes each (i.e. 4x CEx7 LX2160A COM Express modules) Compute Node – NXP 2160A 16-core Cortex-A72 processor for a total of  64 cores per blade. Memory – Up to 64GB ECC DDR4 per compute node or 256GB per blade. Storage – 1x 2.5” NVMe SSD PCIe up to 8TB per compute node, or 32TB per blade …

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Register to the Embedded Online Conference for Free Before February 29th

Events such as the Embedded Linux Conference and Embedded Systems Conference take place in the US and Europe every year. There are plenty of talks and it’s certainly good for networking, but you need to travel to the event and the entrance fee to have access to all session costs several hundred dollars if you book early, and over one thousand dollars if you register close to the date of the event. Most ELC/ELCE videos usually end up on The Linux Foundation YouTube channel, but the Beningo Embedded Group and Embedded Related website decided to organize a similar conference happening online and simply called the “Embedded Online Conference“. The conference offers topics about embedded systems, DSP, machine learning and FPGA and will take place on May 20.  There are currently 17 talks, but they are still calling for talks so more sessions may be added before the actual event. You’ll also be able to ask questions to the speakers. Some …

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Toradex AI Embedded Vision Starter Kit Leverages Amazon Web Services for AI and ML Applications

Toradex, Amazon Web Services (AWS), and NXP Semiconductors collaborated to create the AI Embedded Vision Starter Kit aiming to ease the development of cloud-connected computer vision and machine learning applications in industries such as industrial automation, agriculture, medical equipment, and many more. The AI Embedded Vision Starter Kit includes the following items: Toradex Apalis iMX8 System on Module (SoM) powered by NXP i.MX 8QuadMax applications processor Toradex Ixora Carrier Board Allied Vision Alvium 1500 industrial-grade MIPI CSI-2 camera All required cables and a 12VDC (30W) power supply Full software stack, including source code for running the device as well as for cloud deployment Extensive documentation 50 USD AWS credit The kit will help developers meet the must-have requirements of smart connected devices including secure connectivity, remote monitoring, OTA updates, maximum uptime & reliability, compact form factor, cost-optimized hardware, computer vision and machine learning algorithms optimized for low-power hardware, and more. Alex Dopplinger, industrial marketing manager at NXP explains The step-by-step …

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