The Raspberry Pi AI Camera is a compact camera module based on the Sony IMX500 Intelligent Vision Sensor. The IMX500 combines a 12 MP CMOS image sensor with on-board inferencing acceleration for various common neural network models, allowing users to develop sophisticated vision-based AI applications without requiring a separate accelerator. The AI Camera enhances captured still images or video with tensor metadata, while keeping the Raspberry Pi's processor free for other tasks. Support for tensor metadata in the libcamera and Picamera2 libraries, as well as the rpicam-apps application suite, ensures ease of use for beginners while providing unparalleled power and flexibility for advanced users. The Raspberry Pi AI Camera is compatible with all Raspberry Pi models. Features 12 MP Sony IMX500 Intelligent Vision Sensor Sensor modes: 4056x3040 (@ 10fps), 2028x1520 (@ 30fps) 1.55 x 1.55 µm cell size 78-degree field of view with manually adjustable focus Integrated RP2040 for neural network and firmware management Specifications Sensor Sony IMX500 Resolution 12.3 MP (4056 x 3040 pixels) Sensor size 7.857 mm (type 1/2.3) Pixel size 1.55 x 1.55 μm IR cut filter Integrated Autofocus Manual adjustable focus Focus range 20 cm – ∞ Focal length 4.74 mm Horizontal FOV 66 ±3° Vertical FOV 52.3 ±3° Focal ratio (F-stop) F1.79 Output Image (Bayer RAW10), ISP output (YUV/RGB), ROI, metadata Input tensor maximum size 640 x 640 (H x V) Framerate • 2x2 binned: 2028x1520 10-bit 30fps• Full resolution: 4056x3040 10-bit 10fps Ribbon cable length 20 cm Cable connector 15 x 1 mm FPC or 22 x 0.5 mm FPC Dimensions 25 x 24 x 11.9 mm Downloads Datasheet Documentation

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ESP32-S3-BOX-3 is based on Espressif’s ESP32-S3 Wi-Fi + Bluetooth 5 (LE) SoC, with AI acceleration capabilities. In addition to ESP32-S3’s 512 KB SRAM, ESP32-S3-BOX-3 comes with 16 MB of Quad flash and 16 MB of Octal PSRAM. ESP32-S3-BOX-3 runs Espressif’s own speech-recognition framework, ESP-SR, which provides users with an offline AI voice-assistant. It features far-field voice interaction, continuous recognition, wake-up interruption, and the ability to recognize over 200 customizable command words. BOX-3 can also be transformed into an online AI chatbot using advanced AIGC development platforms, such as OpenAI. Powered by the high-performance ESP32-S3 SoC, BOX-3 provides developers with an out-of-the-box solution to creating Edge AI and HMI applications. The advanced features and capabilities of BOX-3 make it an ideal choice for those in the IIoT industry who want to embrace Industry 4.0 and transform traditional factory-operating systems. ESP32-S3-BOX-3 is the main unit powered by the ESP32-S3-WROOM-1 module, which offers 2.4 GHz Wi-Fi + Bluetooth 5 (LE) wireless capability as well as AI acceleration capabilities. On top of 512 KB SRAM provided by the ESP32-S3 SoC, the module comes with additional 16 MB Quad flash and 16 MB Octal PSRAM. The board is equipped a 2.4-inch 320 x 240 SPI touch screen (the ‘red circle’ supports touch), two digital microphones, a speaker, 3‑axis Gyroscope, 3‑axis Accelerometer, one Type-C port for power and download/debug, a high-density PCIe connector which allows for hardware extensibility, as well as three functional buttons. Features ESP32-S3 WiFi + Bluetooth 5 (LE) Built-in 512 KB SRAM ESP32-S3-WROOM-1 16 MB Quad flash 16 MB Octal PSRAM Included ESP32-S3-BOX-3 Unit ESP32-S3-BOX-3 Sensor ESP32-S3-BOX-3 Dock ESP32-S3-BOX-3 Bracket ESP32-S3-BOX-3 Bread RGB LED module and Dupont wires USB-C cable Downloads GitHub

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The Raspberry Pi AI HAT+ is an expansion board designed for the Raspberry Pi 5, featuring an integrated Hailo AI accelerator. This add-on offers a cost-effective, efficient, and accessible approach to incorporating high-performance AI capabilities, with applications spanning process control, security, home automation, and robotics. Available in models offering 13 or 26 tera-operations per second (TOPS), the AI HAT+ is based on the Hailo-8L and Hailo-8 neural network accelerators. The 13 TOPS model efficiently supports neural networks for tasks like object detection, semantic and instance segmentation, pose estimation, and more. This 26 TOPS variant accommodates larger networks, enables faster processing, and is optimized for running multiple networks simultaneously. The AI HAT+ connects via the Raspberry Pi 5’s PCIe Gen3 interface. When the Raspberry Pi 5 is running a current version of the Raspberry Pi OS, it automatically detects the onboard Hailo accelerator, making the neural processing unit (NPU) available for AI tasks. Additionally, the rpicam-apps camera applications included in Raspberry Pi OS seamlessly support the AI module, automatically using the NPU for compatible post-processing functions. Included Raspberry Pi AI HAT+ (26 TOPS) Mounting hardware kit (spacers, screws) 16 mm GPIO stacking header Downloads Datasheet

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BeagleY-AI is a low-cost, open-source, and powerful 64-bit quad-core single-board computer, equipped with a GPU, DSP, and vision/deep learning accelerators, designed for developers and makers. Users can take advantage of BeagleBoard.org's provided Debian Linux software images, which include a built-in development environment. This enables the seamless running of AI applications on a dedicated 4 TOPS co-processor, while simultaneously handling real-time I/O tasks with an 800 MHz microcontroller. BeagleY-AI is designed to meet the needs of both professional developers and educational environments. It is affordable, easy to use, and open-source, removing barriers to innovation. Developers can explore in-depth lessons or push practical applications to their limits without restriction. Specifications Processor TI AM67 with quad-core 64-bit Arm Cortex-A53, GPU, DSP, and vision/deep learning accelerators RAM 4 GB LPDDR4 Wi-Fi BeagleBoard BM3301 module based on TI CC3301 (802.11ax Wi-Fi) Bluetooth Bluetooth Low Energy 5.4 (BLE) USB • 4x USB-A 3.0 supporting simultaneous 5 Gbps operation• 1x USB-C 2.0 supports USB 2.0 device Ethernet Gigabit Ethernet, with PoE+ support (requires separate PoE+ HAT) Camera/Display 1x 4-lane MIPI camera/display transceivers, 1x 4-lane MIPI camera Display Output 1x HDMI display, 1x OLDI display Real-time Clock (RTC) Supports an external button battery for power failure time retention. It is only populated on EVT samples. Debug UART 1x 3-pin debug UART Power 5 V/5 A DC power via USB-C, with Power Delivery support Power Button On/Off included PCIe Interface PCI-Express Gen3 x1 interface for fast peripherals (requires separate M.2 HAT or other adapter) Expansion Connector 40-pin header Fan connector 1x 4-pin fan connector, supports PWM speed control and speed measurement Storage microSD card slot, with support for high-speed SDR104 mode Tag Connect 1x JTAG, 1x Tag Connect for PMIC NVM Programming Downloads Pinout Documentation Quick start Software

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Seeed Studio XIAO ESP32S3 Sense integrates a camera sensor, digital microphone, and SD card support. Combining embedded ML computing power and photography capability, this development board can be your great tool to get started with intelligent voice and vision AI. Seeed Studio XIAO ESP32S3 Sense is built around a highly-integrated, Xtensa processor ESP32-S3R8 SoC, which supports 2.4 GHz WiFi and low-power Bluetooth BLE 5.0 dual-mode for multiple wireless applications. It has lithium battery charge management capability. As the advanced version of Seeed Studio XIAO ESP32S3, this board comes with a plug-in OV2640 camera sensor for displaying full 1600x1200 resolution. The base of it is even compatible with OV5640 for supporting up to 2592x1944 resolution. The digital microphone is also carried with the board for voice sensing and audio recognition. SenseCraft AI provides various pre-trained Artificial Intelligence (AI) models and no-code deployment to XIAO ESP32S3 Sense. With powerful SoC and built-in sensors, this development board has 8 MB PSRAM and 8 MB Flash on the chip, an additional SD card slot for supporting up to 32 GB FAT memory. These allow the board for more programming space and bring even more possibilities into embedded ML scenarios. Features Powerful MCU Board: Incorporate the ESP32S3 32-bit, dual-core, Xtensa processor chip operating up to 240 MHz, mounted multiple development ports, Arduino/MicroPython supported Advanced Functionality: with OV5640 camera sensor, integrating additional digital microphone Great Memory for more Possibilities: Offer 8 MB PSRAM and 8 MB Flash, supporting SD card slot for external 32 GB FAT memory Outstanding RF performance: Support 2.4 GHz Wi-Fi and BLE dual wireless communication, support 100m+ remote communication when connected with U.FL antenna Thumb-sized Compact Design: 21 x 17.5 mm, adopting the classic form factor of XIAO, suitable for space-limited projects like wearable devices Pretrained Al model from SenseCraft Al for no-code deployment Applications Image processing Speech Recognition Video Monitoring Wearable devices Smart Homes Health monitoring Education Low-Power (LP) networking Rapid prototyping Specifications Processor ESP32-S3R8 Xtensa LX7 dual-core, 32-bit processor that operates at up to 240 MHz Wireless Complete 2.4 GHz Wi-Fi subsystem BLE: Bluetooth 5.0, Bluetooth mesh Built-in Sensors oV2640 camera sensor for 1600x1200 Digital Microphone Memory On-chip 8 MB PSRAM & 8 MB Flash Onboard SD Card Slot, supporting 32 GB FAT lnterface 1x UART, 1x I²C, 1x I²S, 1x SPI, 11x GPIOs (PWM), 9x ADC, 1x User LED, 1x Charge LED, 1x B2B Connector (with 2 additional GPIOs) 1x Reset button, 1x Boot button Dimensions 21 x 17.5 x 15 mm (with expansion board) Power Input voltage (Type-C): 5 V lnput voltage (BAT): 4.2 V Circuit operating Voltage (ready to operate): - Type-C: 5 V @ 38.3 mA - BAT: 3.8 V @ 43.2 mA (with expansion board) Webcam Web application: Type-C: - Average power consumption: 5 V/138 mA - Photo moment: 5 V/341 mA Battery: - Average power consumption: 3.8 V/154 mA - Photo moment: 3.8 V/304 mA Microphone recording & SD card writing: Type-C: - Average power consumption: 5 V/46.5 mA - Peak power consumption: 5 V/89.6 mA Battery: - Average power consumption: 3.8 V/54.4 mA - Peak power consumption: 3.8 V/108 mA Charging battery current: 100 mA Low Power Consumption Model (Supply Power: 3.8 V) Modem Sleep Model: ~44 mA Light Sleep Model: ~5 mA Deep Sleep Model: ~3 mA Wi-Fi Enabled Power Consumption Active Model: ~ 110 mA (with expansion board) BLE Enabled Power Consumption Active Model: ~ 102 mA (with expansion board) Included 1x XIAO ESP32S3 1x Plug-in camera sensor board 1x Antenna Downloads GitHub

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This collection features the best of Elektor Magazine's articles on embedded systems and artificial intelligence. From hands-on programming guides to innovative AI experiments, these pieces offer valuable insights and practical knowledge for engineers, developers, and enthusiasts exploring the evolving intersection of hardware design, software innovation, and intelligent technology. Contents Programming PICs from the Ground UpAssembler routine to output a sine wave Object-Oriented ProgrammingA Short Primer Using C++ Programming an FPGA Tracking Down Microcontroller Buffer Overflows with 0xDEADBEEF Too Quick to Code and Too Slow to Test? Understanding the Neurons in Neural NetworksEmbedded Neurons MAUI Programming for PC, Tablet, and SmartphoneThe New Framework in Theory and Practice USB Killer DetectorBetter Safe Than Sorry Understanding the Neurons in Neural NetworksArtificial Neurons A Bare-Metal Programming Guide Part 1: For STM32 and Other Controllers Part 2: Accurate Timing, the UART, and Debugging Part 3: CMSIS Headers, Automatic Testing, and a Web Server Introduction to TinyMLBig Is Not Always Better Microprocessors for Embedded SystemsPeculiar Parts, the Series FPGAs for BeginnersThe Path From MCU to FPGA Programming AI in Electronics DevelopmentAn Update After Only One Year AI in the Electronics LabGoogle Bard and Flux Copilot Put to the Test ESP32 and ChatGPTOn the Way to a Self-Programming System… Audio DSP FX Processor Board Part 1: Features and Design Part 2: Creating Applications Rust + EmbeddedA Development Power Duo A Smart Object CounterImage Recognition Made Easy with Edge Impulse Universal Garden LoggerA Step Towards AI Gardening A VHDL ClockMade with ChatGPT TensorFlow Lite on Small MicrocontrollersA (Very) Beginner’s Point of View Mosquito DetectionUsing Open Datasets and Arduino Nicla Vision Artificial Intelligence Timeline Intro to AI AlgorithmsPrompt: Which Algorithms Implement Each AI Tool? Bringing AI to the Edgewith ESP32-P4 The Growing Role of Edge AIA Trend Shaping the Future

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OV7740 is a AI Camera powered by Kendryte K210, an edge computing system-on-chip(SoC) with a dual-core 64bit RISC-V CPU and state-of-art neural network processor. Features Dual-Core 64-bit RISC-V RV64IMAFDC (RV64GC) CPU / 400Mhz(Normal) Dual Independent Double Precision FPU 8MiB 64bit width On-Chip SRAM Neural Network Processor(KPU) / 0.8Tops Field-Programmable IO Array (FPIOA) AES, SHA256 Accelerator Direct Memory Access Controller (DMAC) Micropython Support Firmware encryption support On-board Hardware: Flash: 16M Camera :OV7740 2x Buttons Status Indicator LED External storage: TF card/Micro SD Interface: HY2.0/compatible GROVE Applications Face recognition/detection Object detection/classification Obtain the size and coordinates of the target in real-time Obtain the type of detected target in real-time Shape recognition Video recorder Included 1x UNIT-V(include 20cm 4P cable and USB-C cable)

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The HuskyLens AI Camera intuitive design allows the user to control different aspects of the camera just by pressing buttons. You can start and stop learning new objects and even switch between algorithms from the device. To further reduce the need to be connected to a PC the HuskyLens AI Camera comes with a 2-inch display so you can see what's going on in real time. Specifications Processor: Kendryte K210 Image Sensor: OV2640 (2.0 Megapixel Camera) Supply Voltage: 3.3~5.0 V Current Consumption (TYP): 320 mA @ 3.3 V, 230 mA @ 5.0 V (face recognition mode; 80% backlight brightness; fill light off) Connection Interface: UART, I²C Display: 2.0-inch IPS screen with 320x240 resolution Built-in Algorithms: Face Recognition, Object Tracking, Object Recognition, Line Tracking, Color Recognition, Tag Recognition Dimension: 52 x 44.5 mm (2.05 x 1.75') Included 1x HuskyLens Mainboard 6x M3 Screws 6x M3 Nuts 1x Small Mounting Bracket 1x Heightening Bracket 1x Gravity 4-Pin Sensor Cable

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The SparkFun JetBot AI Kit V3.0 is a great launchpad for creating entirely new AI projects for makers, students, and enthusiasts interested in learning AI and building fun applications. It’s straightforward to set up and use and is compatible with many popular accessories. Several interactive tutorials show you how to harness AI's power to teach the SparkFun JetBot to follow objects, avoid collisions, and more. The Jetson Nano Developer Kit (not included in this kit) offers useful tools like the Jetson GPIO Python library and is compatible with standard sensors and peripherals; including some new python compatibility with the SparkFun Qwiic ecosystem. Additionally, the included image is delivered with the advanced functionality of JetBot ROS (Robot Operating System) and AWS RoboMaker Ready with AWS IoT Greengrass already installed. SparkFun’s JetBot AI Kit is the only kit currently on the market ready to move beyond the standard JetBot examples and into the world of connected and intelligent robotics. This kit includes everything you need to get started with JetBot minus a Phillips head screwdriver and an Ubuntu desktop GUI. If you need these, check out the includes tabs for some suggestions from our catalogue. Please be aware that the ability to run multiple neural networks in parallel may only be possible with a full 5V-4A power supply. Features SparkFun Qwiic ecosystem for I²C communication The ecosystem can be expanded using 4x Qwiic connectors on GPIO header Example Code for Basic Motion, Teleoperation, Collision avoidance, & Object Following Compact form factor to optimize existing neural net from NVIDIA 136° FOV camera for machine vision Pre-flashed MicroSD card Chassis assembly offers expandable architecture No soldering required Included 64 GB MicroSD card - pre-flashed SparkFun JetBot image: Nvidia Jetbot base image with the following installed: SparkFun Qwiic python library package Driver for Edimax WiFi adapter Greengrass Jetbot ROS Leopard Imaging 136FOV wide-angle camera & ribbon cable EDIMAX WiFi Adapter SparkFun Qwiic Motor Driver SparkFun Micro OLED Breakout (Qwiic) All hardware & prototyping electronics needed to complete your fully functional robot! Required NVIDIA Jetson Nano Developer Kit Downloads Assembly Guide

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The Raspberry Pi AI HAT+ is an expansion board designed for the Raspberry Pi 5, featuring an integrated Hailo AI accelerator. This add-on offers a cost-effective, efficient, and accessible approach to incorporating high-performance AI capabilities, with applications spanning process control, security, home automation, and robotics. Available in models offering 13 or 26 tera-operations per second (TOPS), the AI HAT+ is based on the Hailo-8L and Hailo-8 neural network accelerators. This 13 TOPS model efficiently supports neural networks for tasks like object detection, semantic and instance segmentation, pose estimation, and more. The 26 TOPS variant accommodates larger networks, enables faster processing, and is optimized for running multiple networks simultaneously. The AI HAT+ connects via the Raspberry Pi 5’s PCIe Gen3 interface. When the Raspberry Pi 5 is running a current version of the Raspberry Pi OS, it automatically detects the onboard Hailo accelerator, making the neural processing unit (NPU) available for AI tasks. Additionally, the rpicam-apps camera applications included in Raspberry Pi OS seamlessly support the AI module, automatically using the NPU for compatible post-processing functions. Included Raspberry Pi AI HAT+ (13 TOPS) Mounting hardware kit (spacers, screws) 16 mm GPIO stacking header Downloads Datasheet

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LuckFox Pico Mini is a compact Linux micro development board based on the Rockchip RV1103 chip, providing a simple and efficient development platform for developers. It supports a variety of interfaces, including MIPI CSI, GPIO, UART, SPI, I²C, USB, etc., which is convenient for quick development and debugging. Features Single-core ARM Cortex-A7 32-bit core with integrated NEON and FPU Built-in Rockchip self-developed 4th generation NPU, features high computing precision and supports int, int8, and int16 hybrid quantization. The computing power of int8 is 0.5 TOPS, and up to 1.0 TOPS with int4 Built-in self-developed third-generation ISP3.2, supports 4-Megapixel, with multiple image enhancement and correction algorithms such as HDR, WDR, multi-level noise reduction, etc. Features powerful encoding performance, supports intelligent encoding mode and adaptive stream saving according to the scene, saves more than 50% bit rate of the conventional CBR mode so that the images from camera are high-definition with smaller size, double the storage space Built-in RISC-V MCU supports low power consumption and fast start-up, supports 250 ms fast picture capture and loading Al model library at the same time to realize face recognition "in one second" Built-in 16-bit DRAM DDR2, which is capable of sustaining demanding memory bandwidths Integrated with built-in POR, audio codec and MAC PHY Specifications Processor ARM Cortex-A7, single-core 32-bit CPU, 1.2 GHz, with NEON and FPU NPU Rockchip 4th-gen NPU, supports int4, int8, int16; up to 1.0 TOPS (int4) ISP Third-gen ISP3.2, up to 4 MP input at 30fps, HDR, WDR, noise reduction RAM 64 MB DDR2 Storage 128 MB SPI NAND Flash USB USB 2.0 Host/Device via Type-C Camera Interface MIPI CSI 2-lane GPIO Pins 17 GPIO pins Power Consumption Low power, RISC-V MCU for fast startup Dimensions 28 x 21 mm Downloads Wiki

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The LuckFox Pico Ultra is a compact single-board computer (SBC) powered by the Rockchip RV1106G3 chipset, designed for AI processing, multimedia, and low-power embedded applications. It comes equipped with a built-in 1 TOPS NPU, making it ideal for edge AI workloads. With 256 MB RAM, 8 GB onboard eMMC storage, integrated WiFi, and support for the LuckFox PoE module, the board delivers both performance and versatility across a wide range of use cases. Running Linux, the LuckFox Pico Ultra supports a variety of interfaces – including MIPI CSI, RGB LCD, GPIO, UART, SPI, I²C, and USB – providing a simple and efficient development platform for applications in smart home, industrial control, and IoT. Specifications Chip Rockchip RV1106G3 Processor Cortex-A7 1.2 GHz Neural Network Processor (NPU) 1 TOPS, supports int4, int8, int16 Image Processor (ISP) Max input 5M @30fps Memory 256 MB DDR3L WiFi + Bluetooth 2.4GHz WiFi-6 Bluetooth 5.2/BLE Camera Interface MIPI CSI 2-lane DPI Interface RGB666 PoE Interface IEEE 802.3af PoE Speaker interface MX1.25 mm USB USB 2.0 Host/Device GPIO 30 GPIO pins Ethernet 10/100M Ethernet controller and embedded PHY Default Storage Medium eMMC (8 GB) Included 1x LuckFox Pico Ultra W 1x LuckFox PoE module 1x IPX 2.4G 2 db antenna 1x USB-A to USB-C cable 1x Screws pack Downloads Wiki

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The starter kit for Jetson Nano is one of the best kits for beginners to get started with Jetson Nano. This kit includes 32 GB MicroSD card, 20 W adapter, 2-pin jumper, camera, and micro-USB cable. Features 32 GB High-performance MicroSD card 5 V 4 A power supply with 2.1 mm DC barrel connector 2-pin jumper Raspberry Pi camera module V2 Micro-B To Type-A USB cable with DATA enabled

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The reComputer J3010 is a compact and powerful edge AI device powered by the NVIDIA Jetson Orin Nano SoM, delivering an impressive 20 TOPS AI performance – up to 40 times faster than the Jetson Nano. Pre-installed with Jetpack 5.1.1, it features a 128 GB SSD, 4x USB 3.2 ports, HDMI, Gigabit Ethernet, and a versatile carrier board with M.2 Key E for WiFi, M.2 Key M for SSD, RTC, CAN, and a 40-pin GPIO header. Applications AI Video Analytics Machine Vision Robotics Specifications Jetson Orin Nano System-on-Module AI Performance reComputer J3010, Orin Nano 4 GB (20 TOPS) GPU 512-core NVIDIA Ampere architecture GPU with 16 Tensor Cores (Orin Nano 4 GB) CPU 6-core Arm Cortex-A78AE v8.2 64-bit CPU 1.5 MB L2 + 4 MB L3 Memory 4 GB 64-bit LPDDR5 34 GB/s (Orin Nano 4 GB) Video Encoder 1080p30 supported by 1-2 CPU cores Video Decoder 1x 4K60 (H.265) | 2x 4K30 (H.265) | 5x 1080p60 (H.265) | 11x 1080p30 (H.265) Carrier Board Storage M.2 Key M PCIe (M.2 NVMe 2280 SSD 128 GB included) Networking Ethernet 1x RJ-45 Gigabit Ethernet (10/100/1000M) M.2 Key E 1x M.2 Key E (pre-installed 1x Wi-Fi/Bluetooth combo module) I/O USB 4x USB 3.2 Type-A (10 Gbps)1x USB 2.0 Type-C (Device Mode) CSI Camera 2x CSI (2-lane 15-pin) Display 1x HDMI 2.1 Fan 1x 4-pin Fan Connector (5 V PWM) CAN 1x CAN Multifunctional Port 1x 40-Pin Expansion header 1x 12-Pin Control and UART header RTC RTC 2-pin, supports CR1220 (not included) Power Supply 9-19 V DC Mechanical Dimensions 130 x 120 x 58.5 mm (with Case) Installation Desktop, wall-mounting Operating Temperature −10°C~60°C Included 1x reComputer J3010 (system installed) 1x Power adapter (12 V / 5 A) Downloads reComputer J301x Datasheet NVIDIA Jetson Devices and carrier boards comparisions reComputer J401 schematic design file reComputer J3010 3D file

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The reComputer J1020 v2 is a compact edge AI device powered by the NVIDIA Jetson Nano 4 GB production module, delivering 0.5 TFLOPs of AI performance. It features a robust aluminum case with a passive heatsink and comes pre-installed with JetPack 4.6.1. The device includes 16 GB of onboard eMMC storage and offers 2x SCI, 4x USB 3.0, M.2 Key M, HDMI, and DP. Applications Computer Vision Machine Learning Autonomous Mobile Robot (AMR) Specifications Jetson Nano 4 GB System-on-Module AI Performance Jetson Nano 4 GB (0.5 TOPS) GPU NVIDIA Maxwel architecture with 128 NVIDIA CUDA cores CPU Quad-core ARM Cortex-A57 MPCore processor Memory 4 GB 64-bit LPDDR4 25.6 GB/s Video Encoder 1x 4K30 | 2x 1080p60 | 4x 1080p30 | 4x 720p60 | 9x 720p30 (H.265 & H.264) Video Decoder 1x 4K60 | 2x 4K30 | 4x 1080p60 | 8x 1080p30 | 9x 720p60 (H.265 & H.264) Carrier Board Storage 1x M.2 Key M PCIe Networking Ethernet 1x RJ-45 Gigabit Ethernet (10/100/1000M) I/O USB 4x USB 3.0 Type-A1x Micro-USB port for device mode CSI Camera 2x CSI (2-lane 15-pin) Display 1x HDMI Type A; 1x DP Fan 1x 4-pin Fan Connector (5 V PWM) CAN 1x CAN Multifunctional Port 1x 40-Pin Expansion header 1x 12-Pin Control and UART header Power Supply DC 12 V/2 A Mechanical Dimensions 130 x 120 x 50 mm (with Case) Installation Desktop, wall-mounting Operating Temperature −10°C~60°C Included reComputer J1020 v2 (system installed) 12 V/2 A power adapter (with 5 interchangeable adapter plugs) Downloads reComputer J1020 v2 datasheet reComputer J1020 v2 3D file Seeed NVIDIA Jetson Product Catalog NVIDIA Jetson Device and Carrier Boards Comparison

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The Waveshare Jetson Nano Development Kit, based on AI computers Jetson Nano (with 16 GB eMMC) and Jetson Xavier NX, provides almost the same IOs, size, and thickness as the Jetson Nano Developer Kit (B01), more convenient for upgrading the core module. By utilizing the power of the core module, it is qualified for fields like image classification, object detection, segmentation, speech processing, etc., and can be used in sorts of AI projects. Specifications GPU 128-core Maxwell CPU Quad-core ARM A57 @ 1.43 GHz RAM 4 GB 64-bit LPDDR4 25.6 GB/s Storage 16 GB eMMC + 64 GB TF Card Video encoder 250 MP/s 1x 4K @ 30 (HEVC) 2x 1080p @ 60 (HEVC) 4x 1080p @ 30 (HEVC) Video decoder 500 MP/s 1x 4K @ 60 (HEVC) 2x 4K @ 30 (HEVC) 4x 1080p @ 60 (HEVC) 8x 1080p @ 30 (HEVC) Camera 1x MIPI CSI-2 D-PHY lanes Connectivity Gigabit Ethernet, M.2 Key E expansion connector Display HDMI USB 1x USB 3.2 Gen 1 Type A 2x USB 2.0 Type A 1x USB 2.0 Micro-B Interfaces GPIO, I²C, I²S, SPI, UART Dimensions 100 x 80 x 29 mm Included 1x JETSON-NANO-LITE-DEV-KIT (carrier + Nano + heatsink) 1x AC8265 dual-mode NIC 1x Cooling fan 1x USB cable (1.2 m) 1x Ethernet cable (1.5 m) 1x 5 V/3 A power adapter (EU) 1x 64 GB TF Card 1x Card reader Documentation Wiki

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