Author: kissdev
The UDOO Bolt V8 is a powerful x86 maker board designed for demanding applications such as AI, machine learning, and edge computing. This board is equipped with an AMD Ryzen™ Embedded V1605B Quad Core/eight Thread CPU, which can reach speeds up to 3.6 GHz, and features AMD Radeon™ Vega 8 Graphics, making it capable of handling intensive graphical and computational tasks[3]. Key Features High Performance CPU and GPU: The AMD Ryzen™ Embedded V1605B processor and Radeon™ Vega 8 graphics provide robust performance for AI and machine learning tasks, competing with mid-range desktop GPUs like the GTX 950[3]. Memory and Storage:…
The Maixduino is a development board that integrates the Kendryte K210 AI processor, designed for applications in artificial intelligence (AI) and the Internet of Things (IoT). Manufactured by Sipeed, the Maixduino combines the power of the K210 with the connectivity features of the ESP32 module, making it a versatile and powerful tool for developers. Key Features of the Maixduino Kendryte K210 AI Processor The heart of the Maixduino is the Kendryte K210, a system-on-chip (SoC) that excels in machine vision and machine hearing applications. The K210 is built using TSMC’s 28nm process, featuring dual-core 64-bit RISC-V processors clocked at 400…
The HiKey970 is an advanced AI development board featuring the HiSilicon Kirin 970 SoC, which includes the HiAI architecture and a dedicated Neural-network Processing Unit (NPU). This board is designed to accelerate AI capabilities and is particularly suitable for developers looking to create AI-based applications. Key Features Processor: The HiKey970 is powered by the HiSilicon Kirin 970 SoC, which includes an ARM Cortex-A73 quad-core CPU running at up to 2.36GHz and an ARM Cortex-A53 quad-core CPU running at up to 1.8GHz. It also features the ARM Mali-G72 MP12 GPU for high-performance graphics processing. Memory and Storage: The board comes with…
NVIDIA Jetson TX2: Powering AI at the Edge The NVIDIA Jetson TX2 is a high-performance, power-efficient embedded AI computing device designed for edge applications[1][3]. This credit card-sized supercomputer-on-a-module delivers exceptional speed and efficiency, making it ideal for deploying artificial intelligence and deep learning capabilities in various scenarios[1][3]. Key Features GPU: 256-core NVIDIA Pascal™ architecture-based GPU[3] CPU: Dual-Core NVIDIA Denver 2 64-Bit CPU and Quad-Core ARM® Cortex®-A57 MPCore[3] Memory: 8GB 128-bit LPDDR4 Memory with 59.7 GB/s bandwidth[3] Storage: 32GB eMMC 5.1[3] Power Consumption: 7.5W / 15W[3] Performance and Efficiency The Jetson TX2 offers twice the performance of its predecessor or can…
The Adafruit BrainCraft HAT is a powerful accessory designed to enhance the Raspberry Pi’s capabilities for machine learning and AI applications[1][2]. This HAT (Hardware Attached on Top) features a 240×240 IPS TFT display, stereo speakers, microphones, and various input/output options, making it an ideal platform for developing audio and video AI projects[1][3]. Key features of the BrainCraft HAT include: 1.54″ IPS TFT display (240×240 resolution) Stereo speaker and headphone outputs Stereo microphone input with a mechanical on/off switch 5-way joystick and button for user interface Three RGB DotStar LEDs for visual feedback Two 3-pin STEMMA connectors and a STEMMA QT…
Banana Pi BPI-M64 is a powerful single-board computer (SBC) with AI capabilities, powered by the Allwinner A64 SoC. This open-source hardware platform is designed to cater to developers and tech enthusiasts who seek a versatile and expandable computing solution. Key Features Processor: The BPI-M64 is equipped with a 64-bit Quad-Core ARM Cortex-A53 CPU clocked at 1.2 GHz, providing robust processing power for various applications[1][2]. Memory: It includes 2GB of DDR3 SDRAM, ensuring smooth multitasking and efficient performance[1][2]. Storage: The board features 8GB of onboard eMMC flash storage and a microSD slot that supports up to 256GB of additional storage[1][2]. Graphics:…
The Xilinx Kria KV260 Vision AI Starter Kit is a comprehensive development platform designed for AI and vision applications, leveraging Xilinx’s adaptive computing technology. This kit is ideal for developers looking to quickly prototype and deploy advanced vision AI solutions without needing extensive hardware design expertise. Key Features Vision Ready: The kit supports multi-camera setups with up to 8 interfaces, including 3 MIPI sensor interfaces and USB cameras. It also features built-in ISP components for image processing and outputs through HDMI and DisplayPort[1]. Flexible Connectivity: It includes 1 Gb Ethernet and USB 3.0/2.0 ports, allowing for versatile connectivity options[1]. Expandable:…
The Google AIY Vision Kit is a do-it-yourself (DIY) kit designed for building AI-powered vision projects. This kit allows users to create an intelligent camera capable of recognizing objects and faces using machine learning. Components and Assembly The AIY Vision Kit includes: – Vision Bonnet – Raspberry Pi Zero WH – Raspberry Pi Camera v2 – Long and short flex cables – Push button and button harness – Micro USB cable – Piezo buzzer – Privacy LED – Micro SD card – Various nuts, standoffs, and a cardboard camera box for assembly Assembly of the kit takes approximately 1.5 hours.…
The LattePanda Alpha is a powerful mini PC that combines an Intel Core processor with an Arduino co-processor, offering the capabilities of a full Windows or Linux computer alongside Arduino development features[1][2]. At its core, the Alpha 800s model utilizes an Intel Core m3-8100Y processor, which can burst up to 3.4GHz, paired with 8GB of LPDDR3 RAM[2][4]. This compact single-board computer measures just 115 x 78 x 14mm, making it highly portable while still providing impressive performance[1]. The integrated Intel HD Graphics 615 supports 4K video output through HDMI and USB-C DisplayPort connections[2][4]. What sets the LattePanda Alpha apart is…
The ODROID-N2 stands out as a powerful single-board computer (SBC) well-suited for AI and machine learning projects[1]. Featuring an Amlogic S922X processor with a quad-core ARM Cortex-A73 CPU cluster and a dual-core Cortex-A53 cluster, the N2 delivers impressive performance for its compact size[1]. Its Mali-G52 GPU further enhances its capabilities for graphics-intensive tasks[1]. With up to 4GB of DDR4 RAM and eMMC storage options ranging from 8GB to 128GB, the N2 provides ample memory and storage for running complex AI algorithms and handling large datasets[1]. The board’s efficient heat dissipation design, utilizing a metal housing as a heatsink, allows for…