BeagleY-AI SBC features TI AM67A vision processor with 4 TOPS AI accelerators

Texas Instruments AM67A single board computer

The BeagleBoard.org Foundation’s BeagleY-AI is an open-source hardware, credit card-sized SBC powered by a Texas Instruments AM67A quad-core Cortex-A53 vision processor with various programmable blocks capable of delivering up to 4 TOPS for AI algorithms. The board ships with 4GB RAM, relies on a microSD card slot for storage and OS booting, implements gigabit Ethernet, WiFi 6, and Bluetooth 5.4 connectivity, and can drive up to three displays via micro HDMI, OLDI (LVDS), and MIPI DSI interfaces. The BeagleY-AI also comes with two MIPI CSI camera interfaces, four USB 3.0 ports, a USB Type-C port, and a 40-pin GPIO header for expansion. We can also see a 16-pin PCIe FPC connector that looks somewhat similar to the 20-pin PCIe connector on the Raspberry Pi 5 but officially supports PCIe Gen3 x1. BeagleY-AI specifications: SoC – Texas Instruments AM67A (J722S) “vision processor” CPU Quad-core 64-bit Arm Cortex-A53 processor at 1.4GHz Arm […]

Ambiq Apollo510 Arm Cortex-M55 MCU delivers up to 30x better power efficiency for AI/ML workloads

Ambiq Apollo Cortex-M55 AI microcontroller

Ambiq Apollo510 Arm Cortex-M55 microcontroller delivers 30 times better power efficiency than typical Cortex-M4 designs and 10 times the performance of the Apollo4 Cortex-M4 sub-threshold microcontroller for AI and ML workloads. The new MCU also comes with 4MB NVM, 3.75MB SRAM, a 2.5D GPU with vector graphics acceleration that’s 3.5 times faster than the Apollo4 Plus, and support for low-power Memory-in-Pixel (MiP) displays. Like all other Ambiq microcontrollers, the Apollo510 operates at sub-threshold voltage for ultra-low power consumption and implements security with the company’s secureSPOT platform with Arm TrustZone technology. Ambiq Apollo510 specifications: MCU Core – Arm Cortex-M55 core up to 250 MHz with Arm Helium MVE, Arm TrustZone, FPU, MPU, 64KB I-cache, 64KB D-cache, 256KB I-TCM (Tighly Coupled Memory), 256KB D-TCM, Graphics – 2.5D GPU clocked at 96 MHz or 250 MHz with vector graphics acceleration, anti-aliasing hardware acceleration,  rasterizer/full alpha blending/texture mapping, texture/framebuffer compression (TSC4, 6, 6A and […]

MaaXBoard OSM93 – Business card-sized SBC features NXP i.MX 93 AI SoC, supports Raspberry Pi HATs

MaaXBoard OSM93 SBC

MaaXBoard OSM93 is a single board computer (SBC) based on a Size-S OSM module powered by an NXP i.MX 93 Cortex-M55/M33 AI SoC and offered in a business card form factor with support for Raspberry Pi HAT boards through a 40-pin GPIO header and mounting holes. The board also comes with 2GB LDDR4, 16GB eMMC flash, MIPI CSI and DSI interfaces for optional camera and display modules, two gigabit Ethernet ports, optional support for WiFi 6, Bluetooth 5.3, and 802.15.4, three USB 2.0 ports, and two CAN FD interfaces with on-board transceivers. MaaXBoard OSM93 specifications: SoC – NXP i.MX93 CPU 2x Arm Cortex-A55 up to 1.7 GHz 2x Arm Cortex-M33 up to 250 MHz GPU – 2D GPU with blending/composition, resize, color space conversion NPU – 1x Arm Ethos-U65 NPU @ 1 GHz up to 0.5 TOPS Memory – 640 KB OCRAM w/ ECC Security – EdgeLock Secure Enclave System […]

Renesas AIK-RA4E1 and AIK-RA6M3 reference kits are designed for accelerated AI/ML development

Renesas AIK RA4E1 and AIK RA6M3 reference

Renesas AIK-RA4E1 and AIK-RA6M3 are two new development boards based on RA-series 32-bit microcontrollers. These new dev boards have multiple reconfigurable connectivity functions to accelerate AI and ML design and development time. Both boards appear similar, but the AIK-RA4E1 uses the R7FA4E110D2CFM MCU, features three Pmod ports, and has no Ethernet support. On the other hand, the AIK-RA6M3 utilizes the R7FA6M3AH3CFC MCU, has six Pmod ports, and includes Ethernet support. Both the boards support full-speed USB and CAN bus. Renesas AIK-RA4E1 and AIK-RA6M3 reference kits specifications (Consolidated): RA4E1 Microcontroller Features: Model: R7FA4E110D2CFM Package: 64-pin LQFP Core: 100 MHz Arm Cortex-M33 SRAM: 128 KB on-chip Code Flash Memory: 512 MB on-chip Data Flash Memory: 8 KB on-chip RA6M3 Microcontroller Features: Model: R7FA6M3AH3CFC Package: 176-pin LQFP Core: 120 MHz Arm Cortex-M4 with FPU SRAM: 640 KB on-chip Code Flash Memory: 2 MB on-chip Data Flash Memory: 64 KB on-chip Connectivity: One USB […]

Rockchip RK3582 is a cost-down version of RK3588S with two Cortex-A76 cores, four Cortex-A55 cores, no GPU

Rockchip RK3582

Rockchip RK3582 hexa-core SoC is pin-to-pin compatible with the popular Rockchip RK3588S octa-core Cortex-A76/A55 SoC, but only features two Cortex-A76 cores, a 5 TOPS NPU (instead of 6 TOPS) and does not come with a 3D GPU. I was first made aware of the Rockchip RK3582 in October 2023 when I was sent a photo of a board allegedly for a TV box, but while the RK3582 still features a 4K video decoder, the lack of a 3D GPU could make it problematic with 3D accelerated user interface. We now have more details with Radxa having released the datasheet and a few more interesting details. Rockchip RK3582 specifications: Hexa-core CPU – 2x Cortex-A76 and 4x Cortex-A55 cores in dynamIQ configuration (frequencies are still shown as TBD in the datasheet) GPU No 3D GPU 2D graphics engine up to 8192×8192 source, 4096×4096 destination AI Accelerator – 5 TOPS NPU 3.0 (Neural […]

Smartcam T1205 – An IP65-rated AI camera with NVIDIA Jetson Orin Nano 40 TOPS system-on-module

NVIDIA Jetson Orin AI camera

SmartCow’s SmartCam T1025 is a powerful AI camera based on the NVIDIA Jetson Orin Nano 8GB system-on-module with 40 TOPS of AI performance. The camera features M12 connectors for gigabit Ethernet, power, and serial interface, and has been certified with an IP65 ingress protection rating for outdoor operation. The camera also comes with 256GB NVMe SSD for the OS (Jetpack 6.0) and data storage and supports 4G LTE and GPS connectivity through an M.2 module. The company also introduced the SmartCam T1023 model compatible with NVIDIA Jetson Nano and Jetson TX2 NX for applications that do not require as much processing power and/or memory as provided by the Jetson Orin Nano AI camera. SmartCam T1025 specifications: System-on-module – NVIDIA Jetson Orin Nano 8GB CPU – 6-core Arm Cortex-A78AE v8.2 64-bit CPU @ 1.5 GHz with 1.5 MB L2 + 4 MB L3 GPU – 1024-core NVIDIA Ampere GPU @ 625 […]

Review of Purple Pi OH – A Rockchip RK3566 SBC tested in 2GB/16GB and 4GB/32GB configurations

Review of Purple Pi OH and Purple Pi OH Pro

Hello, I am going to review the Purple Pi OH boards from Wireless-Tag. The Purple Pi OH is a single-board computer (SBC) mechanically compatible with the Raspberry Pi. They are designed for personal mobile Internet devices and AIoT devices, which can be used in various applications, such as tablets, speakers with screens, and lightweight AI applications. The manufacturer sent me two models. The first model is the Purple Pi OH, which is equipped with 2GB of memory and 16GB of storage space and supports 2.4GHz Wi-Fi. The second model is the Purple Pi OH Pro, equipped with 4GB of memory and 32GB of storage space. This board supports both 2.4GHz and 5GHz Wi-Fi. The other components of both devices are almost the same. They are powered by the Rockchip RK3566 chip, which integrates a quad-core Cortex-A55 processor up to 1.8 GHz, a Mali-G52 GPU from Arm for 3D graphics acceleration, […]

Edge Impulse machine learning platform adds support for NVIDIA TAO Toolkit and Omniverse

Edge Impulse NVIDIA TAO Models

Edge Impulse machine learning platform for edge devices has released a new suite of tools developed on NVIDIA TAO Toolkit and Omniverse that brings new AI models to entry-level hardware based on Arm Cortex-A processors, Arm Cortex-M microcontrollers, or Arm Ethos-U NPUs. By combining Edge Impulse and NVIDIA TAO Toolkit, engineers can create computer vision models that can be deployed to edge-optimized hardware such as NXP I.MX RT1170, Alif E3, STMicro STM32H747AI, and Renesas CK-RA8D1. The Edge Impulse platform allows users to provide their own custom data with GPU-trained NVIDIA TAO models such as YOLO and RetinaNet, and optimize them for deployment on edge devices with or without AI accelerators. NVIDIA and Edge Impulse claim this new solution enables the deployment of large-scale NVIDIA models to Arm-based devices, and right now the following object detection and image classification tasks are available: RetinaNet, YOLOv3, YOLOv4, SSD, and image classification. You can […]

EDATEC Raspberry Pi 5 fanless case