{"product_id":"coral-usb-accelerator-edge-tpu-ml-coprocessor-for-raspberry-pi-4-5-linux-windows-mac-4-tops-2w-for-tensorflow-lite-models","title":"Coral USB Accelerator - Edge TPU ML Coprocessor for Raspberry Pi 4\/5, Linux, Windows, Mac | 4 TOPS @ 2W for TensorFlow Lite Models","description":"\u003cul\u003e\n\u003cli\u003eML accelerator Google Edge TPU coprocessor: 4 TOPS (int8); 2 TOPS per watt\u003c\/li\u003e\n\u003cli\u003eConnector: USB 3.0 Type-C (data\/power)\u003c\/li\u003e\n\u003cli\u003eDimensions: 65 mm x 30 mm\u003c\/li\u003e\n\u003cli\u003ePerforms high-speed ML inferencing: ML accelerator Google Edge TPU coprocessor: 4 TOPS (int8); 2 TOPS per watt.\u003c\/li\u003e\n\u003cli\u003eSupports all major platforms: Connects via USB to any system running Debian Linux (including Raspberry Pi), macOS, or Windows 10.\u003c\/li\u003e\n\u003cli\u003eSupports TensorFlow Lite: No need to build models from the ground up. TensorFlow Lite models can be compiled to run on the Edge TPU.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eThe Coral USB Accelerator is a hardware device developed by Google as part of their Coral project. It is designed to provide on-device AI (artificial intelligence) inference for a variety of edge devices, including single-board computers like the Raspberry Pi and other embedded systems. The on-board Edge TPU is a small ASIC designed by Google that accelerates TensorFlow Lite models in a power efficient manner: it's capable of performing 4 trillion operations per second (4 TOPS), using 2 watts of power—that's 2 TOPS per watt. For example, one Edge TPU can execute state-of-the-art mobile vision models such as MobileNet v2 at almost 400 frames per second. This on-device ML processing reduces latency, increases data privacy, and removes the need for a constant internet connection.\u003c\/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eAI Acceleration\u003c\/strong\u003e: The USB Accelerator is equipped with Google's Edge TPU (Tensor Processing Unit), which is optimized for running machine learning models efficiently. It accelerates AI inference tasks without the need for a cloud connection, making it suitable for edge computing and privacy-sensitive applications.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUSB Connectivity\u003c\/strong\u003e: It connects to a host device, such as a computer or single-board computer, via a USB interface. This enables easy integration into a wide range of hardware platforms. Compatible with USB 2.0 but inferencing speed is slower.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eEdge Processing\u003c\/strong\u003e: The device allows you to run machine learning models directly on the edge device, reducing latency and bandwidth usage, and improving real-time processing capabilities for AI applications.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSupported Frameworks\u003c\/strong\u003e: The Coral USB Accelerator is compatible with TensorFlow Lite, a popular machine learning framework, making it accessible for developers already familiar with TensorFlow.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eVersatility\u003c\/strong\u003e: It can be used for various applications, including image and video classification, object detection, speech recognition, and more.\u003c\/li\u003e\n\u003c\/ol\u003e\n\u003cp\u003eThe Coral USB Accelerator is designed to bring AI capabilities to a wide range of edge devices, making it easier for developers and hobbyists to implement machine learning applications without relying on cloud-based solutions. It is part of Google's broader Coral ecosystem, which includes development tools, software libraries, and pre-trained models to facilitate AI development at the edge.\u003c\/p\u003e\n\u003ch4\u003eFeatures:\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eEasy to use\u003c\/li\u003e\n\u003cli\u003eEasy to connect\u003c\/li\u003e\n\u003cli\u003ePerforms high-speed ML inferencing: ML accelerator Google Edge TPU coprocessor: 4 TOPS (int8); 2 TOPS per watt. The on-board Edge TPU coprocessor is capable of performing 4 trillion operations (tera-operations) per second (TOPS), using 0.5 watts for each TOPS (2 TOPS per watt). For example, it can execute state-of-the-art mobile vision models such as MobileNet v2 at almost 400 FPS, in a power efficient manner. \u003c\/li\u003e\n\u003cli\u003eSupports all major platforms: Connects via USB to any system running Debian Linux (including Raspberry Pi), macOS, or Windows 10.\u003c\/li\u003e\n\u003cli\u003eSupports TensorFlow Lite: No need to build models from the ground up. TensorFlow Lite models can be compiled to run on the Edge TPU.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch4\u003ePackage Includes:\u003c\/h4\u003e\n\u003cp\u003e1 x Coral USB Accelerator - Edge TPU ML Coprocessor for Raspberry Pi 4\/5, Linux, Windows, Mac | 4 TOPS @ 2W for TensorFlow Lite Models\u003c\/p\u003e","brand":"Coral","offers":[{"title":"Default Title","offer_id":44145467490347,"sku":null,"price":12500.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0574\/6796\/1387\/files\/Coral_USB_Accelerator.png?v=1780299385","url":"https:\/\/www.indianhobbycenter.com\/products\/coral-usb-accelerator-edge-tpu-ml-coprocessor-for-raspberry-pi-4-5-linux-windows-mac-4-tops-2w-for-tensorflow-lite-models","provider":"Indian Hobby Center","version":"1.0","type":"link"}