18437769. DATA PATH FOR GPU MACHINE LEARNING TRAINING WITH KEY VALUE SSD simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)

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DATA PATH FOR GPU MACHINE LEARNING TRAINING WITH KEY VALUE SSD

Organization Name

SAMSUNG ELECTRONICS CO., LTD.

Inventor(s)

Joo Hwan Lee of San Jose CA (US)

Yang Seok Ki of Palo Alto CA (US)

DATA PATH FOR GPU MACHINE LEARNING TRAINING WITH KEY VALUE SSD - A simplified explanation of the abstract

This abstract first appeared for US patent application 18437769 titled 'DATA PATH FOR GPU MACHINE LEARNING TRAINING WITH KEY VALUE SSD

Simplified Explanation:

This patent application describes a system and method for machine learning using a GPU and key value storage device. The GPU writes key value requests to its memory, which are then processed by the key value storage device to provide corresponding values.

  • The system involves a GPU with GPU memory and a key value storage device.
  • The GPU writes key value requests to a queue in its memory.
  • The key value storage device reads and processes these requests, providing values corresponding to the keys.
  • This method enhances machine learning processes by efficiently managing key value pairs.

Key Features and Innovation:

  • Integration of GPU and key value storage device for machine learning.
  • Efficient handling of key value requests in GPU memory.
  • Improved performance and speed in processing key value pairs for machine learning algorithms.

Potential Applications:

This technology can be applied in:

  • Deep learning algorithms
  • Image recognition systems
  • Natural language processing tasks

Problems Solved:

  • Streamlining key value processing in machine learning.
  • Enhancing the speed and efficiency of data retrieval in GPU-based systems.

Benefits:

  • Faster processing of key value pairs.
  • Improved performance of machine learning algorithms.
  • Enhanced accuracy in data retrieval and processing.

Commercial Applications:

Potential commercial uses include:

  • Developing advanced AI systems
  • Optimizing data analysis processes
  • Enhancing performance of GPU-based applications

Prior Art:

No specific prior art information provided in the abstract.

Frequently Updated Research:

No information on frequently updated research related to this technology provided in the abstract.

Questions about Machine Learning with GPU and Key Value Storage:

Question 1: How does the integration of a key value storage device improve machine learning processes compared to traditional methods? - The integration of a key value storage device allows for more efficient handling of key value pairs, leading to faster data retrieval and processing in machine learning algorithms.

Question 2: What are the potential limitations of using a GPU and key value storage device in machine learning applications? - Some potential limitations could include the complexity of implementation and potential compatibility issues with existing systems.


Original Abstract Submitted

A system and method for machine learning. The system includes a GPU with a GPU memory, and a key value storage device connected to the GPU memory. The method includes, writing, by the GPU, a key value request to a key value request queue in a input-output region of the GPU memory, the key value request including a key. The method further includes reading, by the key value storage device, the key value request from the key value request queue, and writing, by the key value storage device, in response to the key value request, a value to the input-output region of the GPU memory, the value corresponding to the key of the key value request.

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