Posted on by Leo Zachary - 13 Comments on DongshanPI-D1s – An Allwinner D1s RISC-V development board designed to teach programming

DongshanPI-D1s – An Allwinner D1s RISC-V development board designed to teach programming

DongShanPI D1s

The DongshanPI-D1s development board is comprised of a soldered-on Allwinner D1s RISC-V system-on-module board (SoM) and a carrier board with two 40-pin headers and a 2.0mm dedicated header. This development board is specifically designed to teach programming with a focus on the RISC-V architecture. The development board was designed by 100ask. They previously designed the Dongshan NeZha STU a development board based on the Allwinner D1. The main difference between the two is that 100ask did not include the Ethernet and HDMI interfaces on the DongshanPI-D1s board. The pinout of the headers is also slightly different because they opted to make the headers compatible with the widely used 40-pin GPIO from Raspberry Pi single board computers. DongshanPI-D1s preliminary specifications: D1s Core Lite SoC – Allwinner D1s single-core XuanTie C906 64-bit RISC-V processor @ 1.0 GHz with with 32 KB I-cache + 32 KB D-cache Memory – 64 MB DDR2 (SIP) […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 10 Comments on Quadric Chimera GPNPU IP combines NPU, DSP, and real-time CPU into one single programmable core

Quadric Chimera GPNPU IP combines NPU, DSP, and real-time CPU into one single programmable core

Quadric GPNPU

A typical chip for AI or ML inference would include an NPU, a DSP, a real-time CPU, plus some memory, an application processor, an ISP, and a few more IP blocks. Quadric Chimera GPNPU (general purpose neural processor unit) IP combines the NPU, DSP, and real-time CPU into one single programmable core. According to Quadric, the main benefit of such design is simplifying system-on-chip (SoC) hardware design and subsequent software programming once the chip is available thanks to a unified architecture for machine learning inference as well as pre-and-post processing. Since the core is programmable it should also be future-proof. Three “QB series” Chimera GPNPU cores are available: Chimera QB1 – 1 TOPS machine learning, 64 GOPS DSP capability Chimera QB4 – 4 TOPS ML, 256 GOPS DSP Chimera QB16 – 16 TOPS ML, 1 TOPS DSP Quadric says the Chimera cores can be used with any (modern) manufacturing process […]

ArmSoM CM5 Raspberry Pi CM4 alternative with Rockchip RK3576 SoC
Posted on by Jean-Luc Aufranc (CNXSoft) - 10 Comments on SiFive P670 and P470 RISC-V processors feature RISC-V Vector Extensions

SiFive P670 and P470 RISC-V processors feature RISC-V Vector Extensions

SiFive Performance RISC-V processors Roadmap

SiFive has announced two new RISC-V Performance cores with the P670 and P470 processors with RISC-V Vector Extension for AI/ML, media and sensor processing, and designed for high volume applications such as wearables, smart home, industrial automation, AR/VR, and other consumer devices. The P670 is comparable to the Cortex-A78, and the P470 is comparable to the Cortex-A55. Both support the standardized RISC-V RVA22 profile for better OS compatibility and implement RISC-V Vector v1.0 and Vector Cryptography extensions. The SiFive Performance P470 and P670 share the following features: Full RISC-V RVA22 profile compliance Full, Out-of-Order, RISC-V Vector implementation, based on the ratified RISC-V Vector v1.0 Specification RISC-V Vector Cryptography extensions SiFive WorldGuard system security Support for virtualization, including a separate IOMMU for accelerating virtualized device IO Advanced Interrupt Architecture (AIA) compliant interrupt controller with better support for Message Signal Interrupts (MSI) and virtualization Enhanced scalability with fully coherent multi-core, multi-cluster, with […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 3 Comments on System-on-module combines NXP i.MX 8M Mini Arm CPU and Xilinx Artix-7 FPGA

System-on-module combines NXP i.MX 8M Mini Arm CPU and Xilinx Artix-7 FPGA

NXP Arm + Xilinx FPGA development board

MYIR Tech has launched the MYC-JX8MMA7 system-on-module combining an NXP i.MX 8M Mini quad-core Arm Cortex-A53 processor with an AMD Xilinx XC7A25T Artix-7 FPGA. The 82 x 45mm CPU module comes with 2GB LPDDR4, 8GB eMMC flash, and 32MB QSPI Flash for the Arm processor and 256MB DDR3 and 32MB QSPI Flash for FPGA. It exposes I/Os through an MXM 3.0 edge connector and can operate in the industrial temperature range (-40 to 85°C). MYC-JX8MMA7 CPU module specifications: SoC – NXP i.MX 8M Mini with quad-core Cortex-A53 processor @ up to 1.6 (industrial) or 1.8 GHz, Cortex-M4F real-time core @ 400 MHz, Vivante GC320 and Vivante GCNanoUltra 3D/2D GPUs, 1080p60 H.265, H.264, VP8, VP9 video decoder, 1080p60 H.264 & VP8 video encoder FPGA – AMD Xilinx Artix-7 XC7A25T-2CSG325 with 23,360 logic cells, 3x GTP System Memory and Storage SoC – 2GB LPDDR4, 8GB eMMC flash, and 32MB QSPI Flash FPGA […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 1 Comment on Review of myCobot 280 Pi robotic arm with Python and visual programming

Review of myCobot 280 Pi robotic arm with Python and visual programming

myCobot 280 Pi Conveyor Color Sorting

myCobot 280 Pi is a versatile robotic arm with a 6 degree of freedom design. It was developed by Elephant Robotics using the Raspberry Pi 4 board as the main controller. The robot is compact and delivers stable operation making it ideal for confined spaces. It can also be programmed in a variety of languages, is easy to use, and offers a lot of features. It is suitable for those who are interested in learning how to program a robotic arm controller and for engineering projects. Unboxing myCobot 280 Pi The myCobot 280 Pi arm has a working range of 280 mm, weighs 850 grams, and can handle a payload of up to 250 grams. It is powered by 6 servo motors, one for each degree of freedom, and comes with a 5×5 matrix LED display, and supports LEGO parts as well. Controlled by a Raspberry Pi 4 single board […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 3 Comments on TinyML-CAM pipeline enables 80 FPS image recognition on ESP32 using just 1 KB RAM

TinyML-CAM pipeline enables 80 FPS image recognition on ESP32 using just 1 KB RAM

TinyML-CAM image recognition microcontroller boards

The challenge with TinyML is to extract the maximum performance/efficiency at the lowest footprint for AI workloads on microcontroller-class hardware. The TinyML-CAM pipeline, developed by a team of machine learning researchers in Europe, demonstrates what’s possible to achieve on relatively low-end hardware with a camera. Most specifically, they managed to reach over 80 FPS image recognition on the sub-$10 ESP32-CAM board with the open-source TinyML-CAM pipeline taking just about 1KB of RAM. It should work on other MCU boards with a camera, and training does not seem complex since we are told it takes around 30 minutes to implement a customized task. The researchers note that solutions like TensorFlow Lite for Microcontrollers and Edge Impulse already enable the execution of ML workloads, onMCU boards, using Neural Networks (NNs). However, those usually take quite a lot of memory, between 50 and 500 kB of RAM, and take 100 to 600 ms […]

Rockchip RK3568, RK3588 and Intel x86 SBCs abd SoMs
Posted on by Jean-Luc Aufranc (CNXSoft) - 1 Comment on SMLIGHT SLZB-06 – A Zigbee 3.0 to Ethernet, USB, and WiFi adapter with PoE support

SMLIGHT SLZB-06 – A Zigbee 3.0 to Ethernet, USB, and WiFi adapter with PoE support

SMLIGHT SLZB-06 Zigbee Ethernet USB WiFi adapter

Startup SMLIGHT has launched the SLZB-06 Zigbee 3.0 to Ethernet, USB, and WiFi adapter with PoE support that works out of the box with open-source software such as Home Assistant and Zigbee2MQTT. The device combines Texas Instruments’ СС2652Р microcontroller for Zigbee with ESP32 for WiFi, data transfer to Ethernet or USB, and peripheral functions such as LEDs and a button.The design is complemented with Microchip LAN8720 for Ethernet.   SMLIGHT SLZB-06 specifications: Wireless SoCs Texas Instruments CC2652P1FRGZR Arm Cortex-M4F microcontroller @ 48 MHz with 352KB flash, 256KB ROM for protocols and library functions,  80+8KB SRAM, integrated power amplifier, Bluetooth 5.2 Low Energy and 802.15.4 radios Espressif Systems ESP32-DOWDQ5-V3 dual-core processor @ 240MHz with 448 KB ROM, 520 KB SRAM, 16 KB SRAM in RTC, WiFi and BLE connectivity Connectivity Ethernet RJ45 port with PoE support (IEEE 802.3af) implemented through Microchip LAN8720 10/100M Ethernet controller 2.4 GHz WiFi up to 150 […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 12 Comments on LILYGO T7-S3 ESP32-S3 board with 16MB flash, 8MB PSRAM, LiPo battery support sells for under $10 (Promo)

LILYGO T7-S3 ESP32-S3 board with 16MB flash, 8MB PSRAM, LiPo battery support sells for under $10 (Promo)

LILYGO T7-S3 ESP32-S3 development board

LILYGO has launched another ESP32-S3 development board. The T7-S3 is equipped with an ESP32-S3-WROOM-1 module with 16MB flash, 8MB PSRAM, and a PCB antenna. The board is fairly compact at 3.9×3.1 cm, can be powered through a USB-C port or a LiPo battery with charging support, and offers 40 through holes plus a Qwicc/QT I2C connector for expansion. LILYGO T7-S3 specifications: Wireless module – ESP32-S3-WROOM-1-N16R8 module with SoC – Espressif Systems ESP32-S3 dual-core Xtensa LX7 processor @ up to 240 MHz integrating vector instructions for AI acceleration, 512 KB SRAM, WiFi 4 and Bluetooth 5.0 LE & Mesh connectivity Memory – 8MB PSRAM Storage – 16MB SPI Flash USB – USB Type-C port Expansion 2x 20-pin headers with up to 29x GPIOs, 20x ADC, UART, SPI, Touch interface, 5V, 3.3V, and GND 4-pin Qwiic/QT I2C connector Misc – User LED, charging LED (blue), reset and boot buttons, power on/off switch […]

Boardcon Rockchip RK3588S SBC with 8K, WiFI 6, 4G LTE, NVME SSD, HDMI 2.1...