20240020537. METHODOLOGY TO GENERATE EFFICIENT MODELS AND ARCHITECTURES FOR DEEP LEARNING simplified abstract (Groq, Inc.)

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METHODOLOGY TO GENERATE EFFICIENT MODELS AND ARCHITECTURES FOR DEEP LEARNING

Organization Name

Groq, Inc.

Inventor(s)

Andrew Chaang Ling of Toronto (CA)

Aidan Robert Byron Wood of Toronto (CA)

Baorui Zhou of Toronto (CA)

Andrew Esper Bitar of Toronto (CA)

Jonathan Alexander Ross of Palo Alto CA (US)

METHODOLOGY TO GENERATE EFFICIENT MODELS AND ARCHITECTURES FOR DEEP LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240020537 titled 'METHODOLOGY TO GENERATE EFFICIENT MODELS AND ARCHITECTURES FOR DEEP LEARNING

Simplified Explanation

The patent application describes a system and method for generating an efficient neural network model architecture and an efficient processor for deep learning in an artificial intelligence (AI) processor. The system and method involve creating a processor architecture as a companion to the neural network model by composing multiple processor architectures to enable architectural exploration. This compilation can be implemented for any spatial processor architecture using ASIC or FPGA devices. The processor architecture can be uniquely defined for a selected ML or AI model without the need to update the software compiler.

  • The patent application describes a system and method for generating an efficient neural network model architecture and an efficient processor for deep learning in an AI processor.
  • The system and method involve creating a processor architecture as a companion to the neural network model.
  • Multiple processor architectures are composed to enable architectural exploration.
  • The compilation can be implemented for any spatial processor architecture using ASIC or FPGA devices.
  • The processor architecture can be uniquely defined for a selected ML or AI model without the need to update the software compiler.

Potential applications of this technology:

  • Efficient deep learning in AI processors.
  • Improved performance and energy efficiency in neural network model architectures.
  • Accelerated development of AI processors and neural network models.

Problems solved by this technology:

  • Inefficient neural network model architectures.
  • Limited exploration of processor architectures for deep learning.
  • Difficulty in defining processor architectures for specific ML or AI models.

Benefits of this technology:

  • Improved performance and energy efficiency in deep learning tasks.
  • Flexibility in defining processor architectures for specific ML or AI models.
  • Accelerated development and optimization of AI processors and neural network models.


Original Abstract Submitted

a system and method of generating an efficient neural network model architecture and an efficient processor for deep learning in an artificial intelligence (ai) processor are provided. the system and method to create the processor architecture as a companion to the neural network model by composing a plurality of processor architectures to enable architectural exploration. the compilation can be implemented for any arbitrary spatial processor architecture using either asic or fpga devices. the processor architecture can be uniquely defined for a selected ml or ai model without having to update the software compiler.

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