{"product_id":"google-coral-usb-edge-tpu-ml-accelerator-coprocessor-for-raspberry-pi-and-other-embedded-single-board-computers","title":"Google Coral USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers","description":"\u003cp\u003e\u003cb\u003eBrand:\u003c\/b\u003e Google Coral\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFeatures:\u003c\/b\u003e \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eSpecifications: Arm 32-bit Cortex-M0+ microprocessor (MCU): up to 32 MHz max 16 KB flash memory with ECC 2 KB RAM connections: USB 3.1 (Gen 1) port and cable (SuperSpeed, 5Gb\/s transfer speed)\u003c\/li\u003e\n\u003cli\u003eFeatures: Google Edge TPU ML acceleration coprocessor, USB 3.0 Type-C female, supports Debian Linux to host CPU, models are built with TensorFlow Supports MobileNet and Inception architectures through custom architectures are possible. Compatible with Google Cloud\u003c\/li\u003e\n\u003cli\u003eSpecifications: Arm 32-bit Cortex-M0+ Microprocessor (MCU): Up to 32 MHz max 16 KB Flash memory with ECC 2 KB RAM Connections: USB 3.1 (gen 1) port and cable (SuperSpeed, 5Gb\/s transfer speed)\u003c\/li\u003e\n\u003cli\u003eFeatures: Google Edge TPU ML accelerator coprocessor, USB 3.0 Type-C socket, Supports Debian Linux on host CPU, Models are built using TensorFlow. Fully supports MobileNet and Inception architectures through custom architectures are possible. Compatible with Google Cloud.\u003c\/li\u003e\n\u003cli\u003eFeatures: Google Edge TPU ML accelerator coprocessor, USB 3.0 Type-C socket, Supports Debian Linux on host CPU, Models are built using TensorFlow. Full supports MobileNet and Inception architectures through custom architectures are possible. Compatible with Google Cloud.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003emodel number:\u003c\/b\u003e Coral-USB-Accelerator\u003c\/p\u003e\u003cp\u003e\u003cb\u003ePart Number:\u003c\/b\u003e Coral-USB-Accelerator\u003c\/p\u003e\u003cp\u003e\u003cb\u003eDetails:\u003c\/b\u003e Coral USB Accelerator brings powerful ML (machine learning) inferencing capabilities to existing Linux systems. Featuring the Edge TPU, a small ASIC designed and built by Google, the USB Accelerator provides high performance ML inferencing with a low power cost over a USB 3.0 interface. For example, it can execute state-of-the-art mobile vision models, such as MobileNet v2 at 100+ fps, in a power-efficient manner. This allows fast ML inferencing to embedded AI devices in a power-efficient and privacy-preserving way.\u003cbr\u003e\nModels are developed in TensorFlow Lite and then compiled to run on the USB Accelerator.\u003cbr\u003e\nEdge TPU key benefits:\u003cbr\u003e\nHigh speed TensorFlow Lite inferencing\u003cbr\u003e\nLow power\u003cbr\u003e\nSmall footprint\u003cbr\u003e\nFeatures\u003cbr\u003e\nGoogle Edge TPU ML accelerator coprocessor\u003cbr\u003e\nUSB 3.0 Type-C socket\u003cbr\u003e\nSupports Debian Linux on host CPU\u003cbr\u003e\nModels are built using TensorFlow. Fully supports MobileNet and Inception architectures though custom architectures are possible\u003cbr\u003e\nCompatible with Google Cloud\u003cbr\u003e\nSpecifications\u003cbr\u003e\nArm 32-bit Cortex-M0+ Microprocessor (MCU): Up to 32 MHz max 16 KB Flash memory with ECC 2 KB RAM\u003cbr\u003e\nConnections: USB 3.1 (gen 1) port and cable (SuperSpeed, 5Gb\/s transfer speed)\u003cbr\u003e\nIncluded cable is USB Type-C to Type-A\u003cbr\u003e\nCoral, a division of Google, helps build intelligent ideas with a platform for local AI.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eEAN:\u003c\/b\u003e 0608614201389\u003c\/p\u003e\u003cp\u003e\u003cb\u003ePackage Dimensions:\u003c\/b\u003e 5.4 x 4.1 x 1.3 inches\u003c\/p\u003e","brand":"Google Coral","offers":[{"title":"Default Title","offer_id":45038666842320,"sku":"B07R53D12W","price":99.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0668\/7401\/5952\/files\/61J05USFjaL.jpg?v=1781396810","url":"https:\/\/go-go-gadgets.com\/products\/google-coral-usb-edge-tpu-ml-accelerator-coprocessor-for-raspberry-pi-and-other-embedded-single-board-computers","provider":"Go-Go-Gadgets","version":"1.0","type":"link"}