development board News - Page 95 of 260 - CNX Software

May 29, 2019May 30, 2019 by Jean-Luc Aufranc (CNXSoft) - 3 Comments

ASUS Tinker Edge T & CR1S-CM-A SBC to Feature Google Coral Edge TPU & NXP i.MX 8M Processor

A few months ago, Google introduced its Coral development board and USB accelerator powered by their Edge TPU delivering up to 4 TOPS and optimized for Tensorflow Lite. ASUS and Google have now partnered to bring more solutions powered by Coral Edge TPU namely ASUS Tinker Edge T board for makers and hobbyists, CR1S-CM-A SBC for industrial projects, and even a full computer fitted with a Google Edge TPU PCIe card. ASUS Tinker Edge T ASUS Tinker Edge T preliminary specifications: SoC – NXP i.MX 8M quad-core Arm Cortex-A53 processor with Arm Cortex-M4F real-time core, GC7000 Lite 3D GPU ML accelerator – Google Edge TPU co-processor System Memory – 1GB LPDDR4 RAM Storage – 8 GB eMMC flash Connectivity – Gigabit Ethernet port, Wi-Fi 2×2 MIMO (802.11b/g/n/ac 2.4/5GHz), and Bluetooth 4.1 Video Output – MIPI DSI connector, and HDMI output Camera I/F – 2x MIPI CSI 2 interfaces for stereoscopic […]

May 22, 2019May 22, 2019 by Jean-Luc Aufranc (CNXSoft) - 5 Comments

Maixduino SBC Combines RISC-V AI, Arduino Form Factor, and ESP32 Wireless Module

Last year RISC-V cores made it into low-cost hardware with neural network and audio accelerator to speed up artificial intelligence workloads at the edge such as object recognition, and speech processing. More precisely, Kendryte K210 dual-core RISC-V processor was found in Sipeed MAIX modules and boards going for $5 and up. Since then a few other variants and kits have been made available including Seeed Studio Grove AI HAT that works connected to a Raspberry Pi or in standalone mode. Seeed Studio has now released another board with Kendryte K210 RISC-V AI processor, but based on Arduino UNO form factor and equipped with an ESP32 module for WiFi and Bluetooth connectivity. Meet Sipeed Maixduino SBC. Sipeed Maixduino specifications: AI Module – Sipeed M1 with Kendryte K210 dual-core RISC-V processor @ 600 MHz, KPU Convolutional Neural Network (CNN) hardware accelerator, APU audio hardware accelerator, 8 MB general purpose SRAM including 5.9MB […]

May 22, 2019 by Jean-Luc Aufranc (CNXSoft) - 5 Comments

Onion Omega2 LTE Board Combines WiFi, 4G LTE and GNSS Connectivity (Crowdfunding)

Onion has launched several compact OpenWrt WiFi boards for IoT projects over the years starting with their Onion Omega board in 2015, and the latest so far being Onion Omega2 Pro with a massive 8GB storage. The company has now launched a new model with 4G LTE connectivity called Onion Omega2 LTE, based on Omega2S+ module, and also featuring GNSS global satellite positioning capability. Onion Omega2 LTE specifications: WiFi Module – Onion Omega2S+ IoT computer module with MediaTek MT7688 MIPS CPU @ 580 MHz, 128 MB RAM, 32 MB storage Cellular and GNS Module – Quectel EC25 LTE Cat 4 modem that delivers 150 Mbps downlink and 50 Mbps uplink data rates, provided either for North America or global markets External Storage – MicroSD card slot up to 2 TB (once such cards become available…) Connectivity 2.4 GHz 802.11 b/g/n Wi-Fi 4 + onboard 2 dBi direction chip antenna & […]

May 20, 2019May 31, 2019 by Jean-Luc Aufranc (CNXSoft) - 30 Comments

Top 5 Raspberry Pi Alternatives in 2019

The Raspberry Pi boards are great little Arm Linux SBC’s with a low price and great community support. But the Broadcom processors used in the boards are also fairly old so they may not be suitable for some projects. If your application needs or would benefit from modern features such as memory over 1GB RAM, an actual Gigabit Ethernet port, USB 3.0 port(s), 4K video output, or H.265 hardware video decoding you’ll have to find Raspberry Pi alternatives. I’ll point out 5 alternative single board computers in this post from the point of view of existing Raspberry Pi users, who will want good software support and a board with the same or similar form factor as Raspberry Pi 3 Model B/B+ in order to reuse their add-on boards and/or enclosures. Price has to be competitive as well, so nothing above $80 will be mentioned. The boards are not listed in […]

May 19, 2019February 26, 2023 by Jean-Luc Aufranc (CNXSoft) - 6 Comments

Arduino Introduces Four New Nano Boards with WiFi, BLE, Sensors, and/or HW Crypto

The Maker Faire 2019 is now taking place in San Mateo, California, United States, and Arduino has made an habit of announcing new products during the events. This year is no different as the company has announced a new Nano Family with four boards: Arduino Nano Every powered by a Microchip ATMega4809 AVR microcontroller Arduino Nano 33 IoT with secure internet connectivity thanks to a U-blox ESP32 WiFi module and ATECC608A HW crypto chip Arduino Nano 33 BLE with Bluetooth connectivity via U-blox NINA B306 module based on Nordic Semi nRF52840 Bluetooth 5.0 chip Arduino Nano 33 BLE Sense with a design similar to Nano 33 BLE but some extra on-board sensors. Arduino Nano Every Specifications: MCU – Microchip ATMega4809 AVR microcontroller @ 20 MHz with 48KB flash, 6KB SRAM, 256 Bytes EEPROM USB to Serial via Microchip ATSAMD11D14A Arm Cortex-M0+ USB MCU 2x 15-pin expansion headers with GPIO, SPI, […]

May 17, 2019June 23, 2020 by Jean-Luc Aufranc (CNXSoft) - 1 Comment

Programmable-Air is an Arduino Nano Powered Pneumatics Kit (Crowdfunding)

Programmable-Air Arduino Nano Pneumatics Kit

Programmable-Air is a hardware toolkit that allows you to inflate and deflate objects. Powered by an Arduino Nano board, the kit comes with high pressure and vacuum pumps, valves, and a pressure sensor that enables users to experiement and learn about pneumatics and inflatables. Some of the potential applications include vacuum pick and place machines, soft robots able to walk, soft robotics hands, using a syringe as a linear actuator, inflatable bras, arts projects and more. Programmable-Air specifications: MCU Board – Arduino Nano board based on Microchip ATMega328P microcontroller Pneumatics – Full channel control, i.e. high pressure through atmospheric pressure to vacuum output in the same tube. Max pressure – 0.5 atmosphere (7.5 PSI / 50 kPa) Min Pressure – -0.5 atmosphere (-7.5 PSI / -50 kPa) Flow rate: 2 liters per minute per motor Expansion Grove I²C connector to easily add sensors (Not shown in prototype photo above) Expandable […]

May 16, 2019May 16, 2019 by Jean-Luc Aufranc (CNXSoft) - 1 Comment

Fusion for TIVA v8 Development Board Enables Debugging & Programming over WiFi

Texas Instruments TIVA Arm Cortex-M4 MCU family was first introduced in 2013, I tested a TIVA Launchpad the following years, and since microcontrollers have usually a long life span they are still in use today, and should still be available for many years. I’m writing about this TI MCU family today because MikroElektronika has just announced Fusion for TIVA V8 development board for TI TIVA, Stellaris and MSP432 microcontrollers with plenty of I/Os including some MikroBus expansion slots, as well as support for debugging and programming over WiFi in addition to the usual USB-UART interface. Fusion for TIVA v8 board specifications: MCU – Socket for MikroElektronika MCU CARD Display Interfaces 2x 20-pin TFT display connector 1x 16-pin LCD connector for 2×16 characters LCD displays in 4-bit mode, optional PWM backlight driving feature Programming – On-board CODEGRIP programmer/debugger, JTAG connector for connecting an external programmer/debugger Connectivity – Ethernet port, WiFI in […]

May 15, 2019 by Jean-Luc Aufranc (CNXSoft) - 12 Comments

ToyBrick RK3399Pro Board Shown to Outperform Jetson Nano SBC

NVIDIA created a lot of buzz when they released $99 Jetson Nano SBC featuring a 128-core Maxwell GPU, and said to deliver 472 GFLOPS of compute performance for running modern AI workloads with a power consumption of around 5 watts. But Jetson Nano is not the only low cost platform to deliver high performance at low power for AI workloads, as for example Rockchip RK3399Pro (RK1808 NPU) found in boards such as Toybrick RK3399Pro is said to deliver 3 TOPS for INT8, 300 GOPS for INT16, and 100 GOPS for FP16 inferences. Those operations per second numbers can be confusing and misleading, so it’s important to check out the performance of actual neural network models, and Rockchip did provide some RK3399Pro benchmarks last year for Inception V3, ResNet34 and VGG16 models comparing the results to Apple A11, Huawei Kirin 970, and NVIDIA Jetson TX2. However, ideally you’d want result from […]