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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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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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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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Build robust, intelligent machines that combine Raspberry Pi computing power with LEGO components. The Raspberry Pi Build HAT provides four connectors for LEGO Technic motors and sensors from the SPIKE Portfolio. The available sensors include a distance sensor, a color sensor, and a versatile force sensor. The angular motors come in a range of sizes and include integrated encoders that can be queried to find their position. The Build HAT fits all Raspberry Pi computers with a 40-pin GPIO header, including – with the addition of a ribbon cable or other extension device — Raspberry Pi 400. Connected LEGO Technic devices can easily be controlled in Python, alongside standard Raspberry Pi accessories such as a camera module. Features Controls up to 4 motors and sensors Powers the Raspberry Pi (when used with a suitable external PSU) Easy to use from Python on the Raspberry Pi

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A Beginner's Guide to AI and Edge Computing Artificial Intelligence (AI) is now part of our daily lives. With companies developing low-cost AI-powered hardware into their products, it is now becoming a reality to purchase AI accelerator hardware at comparatively very low costs. One such hardware accelerator is the Hailo module which is fully compatible with the Raspberry Pi 5. The Raspberry Pi AI Kit is a cleverly designed hardware as it bundles an M.2-based Hailo-8L accelerator with the Raspberry Pi M.2 HAT+ to offer high speed inferencing on the Raspberry Pi 5. Using the Raspberry Pi AI Kit, you can build complex AI-based vision applications, running in real-time, such as object detection, pose estimation, instance segmentation, home automation, security, robotics, and many more neural network-based applications. This book is an introduction to the Raspberry Pi AI Kit, and it is aimed to provide some help to readers who are new to the kit and wanting to run some simple AI-based visual models on their Raspberry Pi 5 computers. The book is not meant to cover the detailed process of model creation and compilation, which is done on an Ubuntu computer with massive disk space and 32 GB memory. Examples of pre-trained and custom object detection are given in the book. Two fully tested and working projects are given in the book. The first project explains how a person can be detected and how an LED can be activated after the detection, and how the detection can be acknowledged by pressing an external button. The second project illustrates how a person can be detected, and how this information can be passed to a smart phone over a Wi-Fi link, as well as how the detection can be acknowledged by sending a message from the smartphone to your Raspberry Pi 5.

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Ready to explore the world around you? By attaching the Sense HAT to your Raspberry Pi, you can quickly and easily develop a variety of creative applications, useful experiments, and exciting games. The Sense HAT contains several helpful environmental sensors: temperature, humidity, pressure, accelerometer, magnetometer, and gyroscope. Additionally, an 8x8 LED matrix is provided with RGB LEDs, which can be used to display multi-color scrolling or fixed information, such as the sensor data. Use the small onboard joystick for games or applications that require user input. In Innovate with Sense HAT for Raspberry Pi, Dr. Dogan Ibrahim explains how to use the Sense HAT in Raspberry Pi Zero W-based projects. Using simple terms, he details how to incorporate the Sense HAT board in interesting visual and sensor-based projects. You can complete all the projects with other Raspberry Pi models without any modifications. Exploring with Sense HAT for Raspberry Pi includes projects featuring external hardware components in addition to the Sense HAT board. You will learn to connect the Sense HAT board to the Raspberry Pi using jumper wires so that some of the GPIO ports are free to be interfaced to external components, such as to buzzers, relays, LEDs, LCDs, motors, and other sensors. The book includes full program listings and detailed project descriptions. Complete circuit diagrams of the projects using external components are given where necessary. All the projects were developed using the latest version of the Python 3 programming language. You can easily download projects from the book’s web page. Let’s start exploring with Sense HAT.

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The Raspberry Pi M.2 HAT+ enables you to connect M.2 peripherals such as NVMe drives and AI accelerators to Raspberry Pi 5’s PCIe 2.0 interface, supporting fast (up to 500 MB/s) data transfer to and from NVMe drives and other PCIe accessories. Raspberry Pi M.2 HAT+ supports devices that have the M.2 M key edge connector, in the 2230 and 2242 form factors. It is capable of supplying up to 3 A to connected M.2 devices. Features Supports single-lane PCIe 2.0 interface (500 MB/s peak transfer rate) Supports devices that use the M.2 M key edge connector Supports devices with the 2230 or 2242 form factor Capable of supplying up to 3 A to connected M.2 devices Power and activity LEDs Included 1x Raspberry Pi 5 M.2 HAT+ 1x Ribbon cable 1x GPIO stacking header 4x Spacers 8x Screws Downloads Datasheet Schematics Assembly instructions

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Unlock your inner Mozart with Piano HAT, a mini musical companion for your Raspberry Pi! Piano HAT is inspired by Zachary Igielman's PiPiano and made with his blessing. It has taken his fabulous idea for a dinky piano add-on for the Raspberry Pi, made it touch-sensitive and added barrels of our trademark Pimoroni polish. Play music in Python, control software synths on your Pi, and take control of hardware synthesizers! Features 16 capacitive touch pads (link each to their own Python function!) 13 piano keys (a full octave) Octave up/down buttons Instrument cycle button (great for use with synthesizers) 16 bright white LEDs (let them light automagically, or take control with Python) 2x Microchip CAP1188 capacitive touch driver chips Use it to control software or hardware synths over MIDI Compatible with all 40-pin header Raspberry Pi models Comes fully assembled Downloads Python library Pinout

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A meteorologically minded Raspberry Pi HAT designed to make hooking up weather sensors a breeze (or a squall, or a gale). Weather HAT is an all-in-one solution for hooking up climate and environmental sensors to a Raspberry Pi. It has a bright 1.54' LCD screen and four buttons for inputs. The onboard sensors can measure temperature, humidity, pressure and light. The sturdy RJ11 connectors will let you easily attach wind and rain sensors. It will work with any Raspberry Pi with a 40-pin header. You could install it outside in a suitable weatherproof enclosure and connect to it wirelessly – logging the data locally or piping it into Weather Underground, a MQTT broker or a cloud service like Adafruit IO. Alternatively, you could house your weather Pi inside and run wires to your weather sensors outside - making use of the nice screen to display readouts. Features 1.54' IPS LCD screen (240 x 240) Four user-controllable switches BME280 temperature, pressure, humidity sensor (datasheet) LTR-559 light and proximity sensor (datasheet) Nuvoton MS51 microcontroller with inbuilt 12-bit ADC (datasheet) RJ11 connectors for connecting wind and rain sensors (optional) HAT-format board Fully-assembled Compatible with all 40-pin header Raspberry Pi models Downloads Python library Schematic Included Weather HAT 2 x 10 mm standoffs

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This is an I/O expansion kit designed for Raspberry Pi, which provides 5 sets of 2x20 pinheaders, that means a handy way to 'stack' multi different HATs together, and use them as a specific combination / project. Features Standard Raspberry Pi connectivity, directly pluggable OR through ribbon cable 5 sets of 2x20 pinheaders, connect multi HATs together USB external power port, provides enough power supply for multi HATs Clear and descriptive pin labels for easy use Reserved jumper pads on the bottom side, pin connections are changeable by soldering, to avoid pin conflicts Note: make sure there are no any pin conflicts between the HATs you want to use together before connecting. Specifications Dimensions: 183 × 65 mm Mounting hole size: 3 mm Included 1x Stack HAT 1x Ribbon cable 40-Pin 1x 2x20 male pinheader 1x RPi screws pack (4pcs) x1

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NetPi is the perfect solution for your Raspberry Pi Pico's connectivity needs. It's an Ethernet HAT that enables your Pico to easily connect to the internet. With support for various internet protocols such as TCP, UDP, WOL over UDP, ICMP, IPv4, and more, NetPi can create IoT devices, robots, home automation systems, and industrial control systems. It has four independent SOCKETs that can be used simultaneously, and it also supports SOCKET-less commands like ARP-Request and PING-Request. The Ethernet HAT is equipped with 10Base-T/100Base-TX Ethernet PHY and auto-negotiation for a full and half duplex with 10 and 100-based connections. NetPi is ideal for various applications. With NetPi, you can now support hardwired internet protocols like TCP, UDP, ICMP, and more. Enjoy four independent sockets for simultaneous connections and perform socket-less commands like ARP-Request and PING-Request. NetPi also supports Ethernet power down mode and wake on LAN over UDP for energy-saving. NetPi is equipped with a 10Base-T/100Base-TX Ethernet PHY and supports auto-negotiation for a full and half duplex with 10 and 100-based connections. The device features network indicator LEDs for full/half duplex, link, 10/100 speed, and active status. Features Compatible with Raspberry Pi Pico (W) Built-in RJ45 with Transformer: Ethernet Port Support 4 independent SOCKETs simultaneously Support Hardwired TCP/IP Protocols: TCP, UDP, ICMP, IPv4, ARP, IGMP, PPPoE Ethernet power down mode and Wake on LAN over UDP for energy-saving 10Base-T/100Base-TX Ethernet PHY with auto-negotiation for full and half duplex with 10 and 100-based connections Network indicator LEDs for full/half duplex, link, 10/100 speed, and active status RP2040 pins breakout with female pin header for other shield and peripheral interfacing 1.3' TFT LCD (240 x 240) and a 5-way joystick for user experience SPI, I²C, UART interfacing Dimensions: 74.54 x 21.00 mm Applications Internet of Things (IoT) devices Industrial automation and control systems Home automation and smart home systems Remote monitoring and data logging systems Robotics and autonomous systems Networked sensor systems Building automation and energy management systems Security and access control systems Downloads GitHub

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A Beginner's Guide to AI and Edge Computing Artificial Intelligence (AI) is now part of our daily lives. With companies developing low-cost AI-powered hardware into their products, it is now becoming a reality to purchase AI accelerator hardware at comparatively very low costs. One such hardware accelerator is the Hailo module which is fully compatible with the Raspberry Pi 5. The Raspberry Pi AI Kit is a cleverly designed hardware as it bundles an M.2-based Hailo-8L accelerator with the Raspberry Pi M.2 HAT+ to offer high speed inferencing on the Raspberry Pi 5. Using the Raspberry Pi AI Kit, you can build complex AI-based vision applications, running in real-time, such as object detection, pose estimation, instance segmentation, home automation, security, robotics, and many more neural network-based applications. This book is an introduction to the Raspberry Pi AI Kit, and it is aimed to provide some help to readers who are new to the kit and wanting to run some simple AI-based visual models on their Raspberry Pi 5 computers. The book is not meant to cover the detailed process of model creation and compilation, which is done on an Ubuntu computer with massive disk space and 32 GB memory. Examples of pre-trained and custom object detection are given in the book. Two fully tested and working projects are given in the book. The first project explains how a person can be detected and how an LED can be activated after the detection, and how the detection can be acknowledged by pressing an external button. The second project illustrates how a person can be detected, and how this information can be passed to a smart phone over a Wi-Fi link, as well as how the detection can be acknowledged by sending a message from the smartphone to your Raspberry Pi 5.

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