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SOM System-On-Modules - SOM Google Edge TPU ML Compute Accelerator, Integrate The Edge TPU into Legacy and New Systems Using a Standard Half-Mini PCIe
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SOM System-On-Modules - SOM Google Edge TPU ML Compute Accelerator, Integrate The Edge TPU into Legacy and New Systems Using a Standard Half-Mini PCIe
Performs high-speed ML inferencing at 4 trillion operations per second
SOM System-On-Modules - SOM Google Edge TPU ML Compute Accelerator, Integrate The Edge TPU into Legacy and New Systems Using a Standard Half-Mini PCIe
Item #: 74093935

SOM System-On-Modules - SOM Google Edge TPU ML Compute Accelerator, Integrate The Edge TPU into Legacy and New Systems Using a Standard Half-Mini PCIe

Item #: 74093935

MMK 264902

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Performs high-speed ML inferencing at 4 trillion operations per second
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What Stands Out

AI Integration
Seamlessly integrates Google's Edge TPU into both legacy and new systems, enabling advanced machine learning capabilities without extensive hardware redesign.
Compact Design
The half-mini PCIe form factor ensures a space-efficient solution suitable for various applications while maintaining high-performance processing.
Enhanced Performance
Offers a significant boost in ML compute power, allowing users to run complex AI algorithms efficiently, enhancing overall system capabilities.

Product Details

Shop SOM System-On-Modules - SOM Google Edge TPU ML Compute Accelerator, Integrate The Edge TPU into Legacy and New Systems Using a Standard Half-Mini PCIe online at a best price in Myanmar. B0844WRL58
  • Integrates Google Edge TPU coprocessor into legacy and new systems using a standard half-size Mini PCIe connector
  • Edge TPU coprocessor measures 30 x 26.8 mm and supports TensorFlow Lite
  • Capable of performing 4 trillion operations per second (TOPS) using 0.5 watts for each TOPS
  • Compatible with Debian Linux and supports TensorFlow Lite models
  • Integrates with any Debian-based Linux system with a compatible card module slot
  • Supports AutoML Vision Edge for building and deploying custom image classification models
Item Weight0.5 lbs (230 grams)

Who Should Buy?

Suitable For
  • Machine Learning Developers

    Ideal for developers creating AI applications, leveraging Edge TPU for efficient ML model execution and processing.

  • Embedded Systems Engineers

    Great for engineers working on embedded systems, facilitating easy integration of AI capabilities into existing platforms.

  • IoT Application Designers

    Perfect for designing IoT solutions, enabling real-time data processing and analytics with low power consumption.

Not Suitable For
  • General Purpose Computing

    Not suitable for users needing general-purpose computing resources, as it's specialized for ML workflows.

  • Low-Tech Enthusiasts

    Not ideal for hobbyists or low-tech users unfamiliar with hardware integration and machine learning concepts.

  • Budget-Conscious Projects

    May not be suitable for projects with tight budgets due to potential costs associated with integration and hardware.

Product Description

SOM System-On-Modules - SOM Google Edge TPU ML Compute Accelerator, Integrate The Edge TPU into Legacy and New Systems Using a Standard Half-Mini PCIe

Have any Query? Chat with us

Customer Questions & Answers

  • Question: What is the SOM Google Edge TPU ML Compute Accelerator?

    Answer: The SOM Google Edge TPU ML Compute Accelerator is a compact module that enables machine learning capabilities directly at the edge by integrating Google's Tensor Processing Unit (TPU) into various systems. This allows for faster inference and lower latency, making it suitable for applications like image classification, object detection, and natural language processing. By leveraging the Edge TPU, developers can enhance their legacy and new systems with powerful ML features, streamlining workflows in robotics, IoT devices, and edge computing scenarios.
  • Question: How can I integrate the SOM Edge TPU into my existing systems?

    Answer: Integrating the SOM Edge TPU into existing systems is straightforward due to its standard Half-Mini PCIe interface. This modularity means you can easily attach it to compatible hardware without extensive modifications. For example, if you have a legacy device that processes video feeds, adding this module can exponentially enhance its analytic capabilities by processing data on-site, thus reducing response times and increasing efficiency.
  • Question: What are the advantages of using the Edge TPU for machine learning?

    Answer: The Edge TPU offers several advantages for machine learning applications, including optimized performance for inferences, reduced need for data transfer to the cloud, and enhanced privacy by processing data locally. This is particularly beneficial in scenarios where latency is critical, such as autonomous vehicles or real-time surveillance systems. By utilizing the Edge TPU, you can maintain high accuracy while ensuring that sensitive data remains on-premises.
  • Question: What types of applications can benefit from the SOM Edge TPU?

    Answer: Applications across various fields like robotics, smart home devices, and industrial automation can significantly benefit from the SOM Edge TPU. For instance, in smart home automation, the Edge TPU can enable real-time facial recognition for security layers. In agriculture, it might assist in analyzing crop health through image processing. These implementations not only enhance functionality but also provide smarter, data-driven insights.
  • Question: Is the SOM Edge TPU compatible with popular development platforms?

    Answer: Yes, the SOM Edge TPU is designed to work seamlessly with popular development platforms like Raspberry Pi, NVIDIA Jetson, and other Linux-based systems. This compatibility means developers can easily leverage existing resources and community libraries, accelerating the development of innovative applications that integrate machine learning functionalities with familiar tools.
  • Question: What is the power consumption of the SOM Edge TPU?

    Answer: The SOM Edge TPU is engineered for efficiency, consuming very low power while delivering impressive performance. With typical power usage around 2 watts, it enables machine learning solutions that remain economical in energy consumption. This is particularly important for battery-operated devices or applications in remote locations where power resources are limited.
  • Question: Can the SOM Edge TPU be used for real-time analytics?

    Answer: Absolutely! The SOM Edge TPU excels at real-time analytics due to its ability to process data on-site without latency associated with cloud computation. For instance, in a smart surveillance system, it can analyze video feeds instantly to detect intrusions, providing immediate alerts. This capability makes the Edge TPU an optimal choice for applications demanding swift data comprehension and actions.
  • Question: Is it possible to train my models on the Edge TPU?

    Answer: While the Edge TPU is primarily optimized for inference rather than training, you can train your models on more powerful platforms and then deploy them to the Edge TPU for efficient inferencing. This method allows you to harness high-performance capabilities for training while still benefiting from the edge capabilities of the TPU for production-level deployments, ensuring your application runs smoothly and efficiently.
  • Question: What frameworks support the SOM Edge TPU?

    Answer: The SOM Edge TPU supports popular frameworks such as TensorFlow Lite, which is tailored for mobile and edge devices. By using TensorFlow Lite, developers can convert their models to a format compatible with the Edge TPU, facilitating easy integration and deployment. This compatibility streamlines the development process, allowing you to utilize machine learning efficiently across various applications.
  • Question: Where can I buy the SOM System-On-Modules - SOM Google Edge TPU ML Compute Accelerator?

    Answer: You can buy the SOM System-On-Modules - SOM Google Edge TPU ML Compute Accelerator from Ubuy in Myanmar. Ubuy offers a wide variety of electronic components, making it easy for you to find this advanced module alongside many other tech products to enhance your projects.

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Pros

  • High performance ML inference tool
  • Easy integration with legacy systems
  • Efficient power consumption
  • Supports various AI frameworks
  • Compact half-mini PCIe form factor

Cons

  • Limited documentation available

Product Price History

Important information

  • Limitations : For products shipped internationally, please note that any manufacturer warranty may not be valid; manufacturer service options may not be available; product manuals, instructions, and safety warnings may not be in destination country languages; the products (and accompanying materials) may not be designed in accordance with destination country standards, specifications, and labeling requirements; and the products may not conform to destination country voltage and other electrical standards (requiring use of an adapter or converter if appropriate). The recipient is responsible for assuring that the product can be lawfully imported to the destination country. When ordering from Ubuy or its affiliates, the recipient is the importer of record and must comply with all laws and regulations of the destination country.
  • Not all the products listed on Ubuy are for sale, as Ubuy is a global search engine. Products are subject to export/trade regulations.

MMK 264902

Order now and get it around Monday, July 06

QTY:

Secured transactionsecured transaction

Ubuy works hard to protect your security and privacy. Our advanced payment security system ensures confidentiality by encrypting your information during transmission using AES (Advanced Encryption Standards) and SSL (Secure Socket Layer) protocols. Your payment details are 100% secure as we do not share your payment details with third party sellers.

Our Top Logistics Partners
  • fedex
  • dhl
Fastest cross-border delivery

Features & Benefits

  • On-board Edge TPU coprocessor capable of 4 TOPS
  • Executes MobileNet v2 at 400 FPS
  • Power efficient at 2 TOPS per watt
  • Works with Debian Linux
  • Supports TensorFlow Lite
  • Supports AutoML Vision Edge