Debashis Das, Author at CNX Software - Embedded Systems News - Page 21 of 73

Author: Debashis Das

Debashis Das is a technical content writer and embedded engineer with over five years of experience in the industry. With expertise in Embedded C, PCB Design, and SEO optimization, he effectively blends difficult technical topics with clear communication
Posted on by Debashis Das - 17 Comments on $35 Orange Pi 4 Pro – An Allwinner A733 Edge AI SBC with up to 16GB LPDDR5, WiFi 6

$35 Orange Pi 4 Pro – An Allwinner A733 Edge AI SBC with up to 16GB LPDDR5, WiFi 6

Orange Pi 4 Pro – Allwinner A733 Edge AI SBC

Just last week, we wrote about the Orange Pi 6 Plus, a powerful CIX P1 SBC that features up to 64GB LPDDR5 memory. Now, the company has introduced the Orange Pi 4 Pro, an Allwinner A733-based low-cost, low-power SBC,  which is very similar to the Radxa Cubie A7A in terms of architecture, AI capabilities, and use cases. It supports up to 16 GB of LPDDR5 RAM, eMMC modules (16–128 GB), NVMe SSD via an M.2 M-key PCIe 3.0  slot, and microSD storage. Connectivity options include Gigabit Ethernet with PoE, WiFi 6, and Bluetooth 5.4. It also features HDMI 2.0, dual MIPI CSI/DSI interfaces, USB 3.0/2.0 ports, and audio I/O. Target applications include robotics control, industrial automation, smart gateways, NAS, and more. Orange Pi 4 Pro specifications: SoC – Allwinner A733 CPU Dual-core Arm Cortex-A76 @ up to 2.00 GHz Hexa-core Arm Cortex-A55 @ up to 1.79 GHz Single-core RISC-V E902 real-time […]

Posted on by Debashis Das - No Comments on Firefly EC-AGXOrin – Jetson AGX Orin 64GB AI inference system supports up to 8 GMSL2 cameras

Firefly EC-AGXOrin – Jetson AGX Orin 64GB AI inference system supports up to 8 GMSL2 cameras

EC AGXOrin NVIDIA Jetson 275 TOPS Edge AI Computer

Firefly EC-AGXOrin is an NVIDIA Jetson AGX Orin 64GB-powered AI inference system, similar to the AAEON BOXER-8645AI and Vecow RAC-1000 rugged Edge AI systems, and designed for edge AI applications such as in-vehicle computing, robotic control, machine vision, intelligent video analytics, and mobile robots. The device features eight GMSL2 connectors (input via two 4-pin Mini FAKRA interfaces) and supports up to 22-channel 1080p or 8K, 30fps H.265 video decoding. It delivers 275 TOPS of AI performance through the NVIDIA module, and integrates 64GB LPDDR5 RAM, 64GB eMMC storage, and offers M.2 NVMe and MicroSD card storage options. Other ports include a 10GbE RJ45 jack, five GbE jacks, USB 3.0, HDMI 2.0, RS232, RS485, CAN, and the system also supports WiFi 6, Bluetooth 5.2, 4G/5G cellular connectivity, and GPS/GNSS. Firefly EC-AGXOrin specifications: SoM – Jetson AGX Orin module with CPU – 12-core Arm Cortex-A78AE v8.2 64-bit processor with 3MB L2 + […]

Posted on by Debashis Das - 2 Comments on u-blox MAX-M10N GNSS module supports Low Energy Accurate Positioning (LEAP) mode, firmware upgrades

u-blox MAX-M10N GNSS module supports Low Energy Accurate Positioning (LEAP) mode, firmware upgrades

u blox UBX M10150 KB chip and the MAX M10N module

Following the launch of the UBX-M10150-CC low-power GNSS module, u-blox has expanded its lineup with the MAX-M10N, another ultra-low-power module based on the UBX-M10150-KB GNSS chip. The new module supports firmware upgradeability and offers up to 50% lower power consumption thanks to features such as Low Energy Accurate Positioning (LEAP) and PSMCT cyclic tracking modes. The latest SPG 5.30 firmware also adds support for RTCM correction input for sub-meter positioning accuracy, along with improved spoofing and jamming detection. It also includes data logging and geo-fencing for autonomous tracking and uses AssistNow Predictive and Live Orbits for faster satellite acquisition. The MAX-M10N integrates an LNA and dual SAW filters for better RF sensitivity and interference protection, while maintaining pin compatibility with earlier MAX modules. These features make it suitable for low-power, high-accuracy applications such as wearables, asset tracking, and battery-powered IoT devices. u-blox MAX-M10N GNSS module specifications: Receiver type – u-blox M10 […]

Posted on by Debashis Das - 18 Comments on GIGABYTE AI TOP ATOM – An NVIDIA GB10 desktop AI supercomputer with 1 petaFLOP AI, 10GbE, 128GB RAM

GIGABYTE AI TOP ATOM – An NVIDIA GB10 desktop AI supercomputer with 1 petaFLOP AI, 10GbE, 128GB RAM

GIGABYTE AI TOP ATOM, NVIDIA Grace Blackwell GB10 Superchip

GIGABYTE has introduced the AI TOP ATOM, a compact desktop AI supercomputer with 1 petaFLOP of AI performance, which is very similar to NVIDIA DGX Spark. That’s because it’s also built around the NVIDIA Grace Blackwell GB10 Superchip. Housed in a 1-liter chassis, it’s designed for generative AI, large language models, and machine learning workloads directly on the desktop. The AI TOP ATOM features 128 GB of LPDDR5x unified memory, supports up to 4 TB PCIe Gen5 SSD storage. The 1,000 TOPS (1 petaFLOP) FP4 of AI compute enables it to handle models with up to 200 billion parameters, or 405 billion in a dual-system configuration. Connectivity includes 10GbE networking, Wi-Fi 7, Bluetooth 5.3, HDMI 2.1a, and multiple USB 3.2 Gen 2×2 Type-C ports. GIGABYTE AI TOP ATOM specifications: SoC – NVIDIA GB10 CPU – 20-core Armv9 processor with 10x Cortex-X925 cores and 10x Cortex-A725 cores Architecture – NVIDIA Grace […]

Posted on by Debashis Das - 3 Comments on Upbeat introduces UP201 and UP301 ultra-low power RISC-V MCUs with always-on AI processing

Upbeat introduces UP201 and UP301 ultra-low power RISC-V MCUs with always-on AI processing

Upbeat UP201 and UP301 Series AI MCUs No BG

Upbeat Technology, in collaboration with SiFive, has introduced UP201 and UP301 always-on AI MCUs for ultra-low-power AI and IoT applications such as wearables, drones, and sensor-based systems. The UP201 is designed for compact, battery-driven devices such as smartwatches, hearing aids, and IoT sensor nodes, whereas the UP301 targets AI and vision-based applications for more complex edge systems such as smart glasses, robotics, and industrial AI equipment. Both chips feature a dual-core RISC-V architecture. The SiFive E21 lightweight core is Always-On (AON), managing continuous low-power sensing, whereas the SiFive E34 performance core activates for higher workloads. The MCUs also integrate a 2.5D GPU and various I/O options. UP201 and UP301 MCU specifications: CPU cores (SiFive Essential RISC-V IP cores) E21 core in Always-On (AON) domain for continuous low-power sensing E34 performance core in the Non-AON domain for higher workloads Up to 400 MHz operating frequency Up to 717 DMIPS performance Near-threshold […]

Posted on by Debashis Das - 2 Comments on LILYGO T-2CAN upgrades TTGO T-CAN485 with ESP32-S3, dual Isolated CAN Bus

LILYGO T-2CAN upgrades TTGO T-CAN485 with ESP32-S3, dual Isolated CAN Bus

LILYGO T 2CAN ESP32 S3 dual CAN development board top

LILYGO T-2CAN is an updated version of the earlier TTGO T-CAN485, with a more powerful ESP32-S3 MCU and two isolated CAN bus interfaces. Like its predecessor, it is designed for vehicle diagnostics, industrial CAN monitoring, and wireless CAN-to-cloud gateways. The board features an ESP32-S3 MCU with 16MB Flash and 8MB PSRAM, along with two MCP2515 CAN controllers supporting CAN V2.0B at up to 1 Mb/s. It also offers Qwiic connectors and a 26-pin GPIO header (unpopulated) for expansion, and supports both USB Type-C (5V) and DC (12–24V) power inputs. Additional features include BOOT and RESET buttons, an external antenna connector, and onboard debugging via USB. LILYGO T-2CAN specifications: ESP32-S3-WROOM-1U wireless module SoC –  ESP32-S3 dual-core Tensilica LX7 microcontroller @ up to 240 MHz with 2.4 GHz 802.11n WiFi 4 and Bluetooth 5.0 LE connectivity Memory – 8MB PSRAM Storage – 16MB SPI flash IPEX antenna connector Industrial control interfaces via 2x  […]

Posted on by Debashis Das - 7 Comments on RP2350 Tiny and Tiny XL boards clone Solder Party’s RP2350 stamp layouts at nearly half the price

RP2350 Tiny and Tiny XL boards clone Solder Party’s RP2350 stamp layouts at nearly half the price

RP2350 Tiny and Tiny XL boards

While searching Aliexpress, I stumbled upon the RP2350 Tiny and Tiny XL, both of which are RP2350-based low-cost stamp-size development boards that look exactly like the Solder Party’s “RP2350 Stamp” modules, but are available at nearly half the price. Both modules feature the Raspberry Pi RP2350 dual-core Arm Cortex-M33 microcontroller and an integrated LiPo charging circuitry. The RP2350 Tiny offers 30x GPIOs, while the larger RP2350 Tiny XL breaks out 48x GPIOs, adds PSRAM, and includes SWD/UART JST connectors. Key interfaces include USB host/device, UART, SPI, I²C, PWM, ADC, and PIO state machines, along with Secure Boot and Arm TrustZone. These features make this board suitable for rapid prototyping and testing with CircuitPython support. RP2350 Tiny and Tiny XL specifications: Microcontroller RP2350 Tiny – Raspberry Pi RP2350A MCU, QFN-60; 7×7 mm RP2350 Tiny XL – Raspberry Pi RP2350B MCU,QFN-80; 10×10 mm Memory – 520KB internal RAM, 8KB OTP storage Storage – 16MB […]

Posted on by Debashis Das - 1 Comment on Allwinner T153-based industrial SoM and SBC feature a mix of Arm Cortex-A7 and RISC-V cores

Allwinner T153-based industrial SoM and SBC feature a mix of Arm Cortex-A7 and RISC-V cores

Forlinx FET153 S Allwinner T153 System on Module

Forlinx has recently introduced the Allwinner T153-based FET153-S SoM and the OK153-S SBC. The Allwinner T153 processor features a hybrid architecture that combines a quad-core Arm Cortex-A7 CPU with an XuanTie E907 RISC-V core. The board features up to 1GB DDR3 RAM, 8GB eMMC, triple Gigabit Ethernet, dual CAN-FD, RS-485, and a Local Bus for PSRAM or FPGA expansion. Display options include support for RGB, LVDS, and MIPI DSI interfaces, and camera inputs are offered via parallel or MIPI CSI. Additional I/O include multiple UART, I²C, SPI, I²S, GPADC, and GPIO options, along with Wi-Fi/Bluetooth, a USB Type-C OTG port, and a Mini PCIe socket for 4G modules. Target applications include real-time control and edge computing, industrial automation, IoT gateways, and embedded systems. Forlinx FET153-S system-on-module Specifications: SoC – Allwinner T153 CPU 4x Arm Cortex-A7 cores @ up to 1.6GHz XuanTie E907 RISC-V core @ up to 600MHz GPU – 2D […]