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USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers
USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers
USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers
USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers
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USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers

Brand: Google Coral
Vendor
Usually Dispatched in 2 to 3 Weeks
Regular price
Rs. 13,999.00
Sale price
Rs. 13,999.00
Regular price
Rs. 19,999.00
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Unit price
per 
Tax included. Shipping calculated at checkout.
SKU: PIB07R53D12W

  • About this Item
  • • Specifications: 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)
  • • Features: 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
  • • Specifications: 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)
  • • Features: 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.

Product description

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.

Models are developed in TensorFlow Lite and then compiled to run on the USB Accelerator.

Edge TPU key benefits:
High speed TensorFlow Lite inferencing
Low power
Small footprint

Features
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 though custom architectures are possible
Compatible with Google Cloud

Specifications
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)

Included cable is USB Type-C to Type-A