18289292. GENERATING AND GLOBALLY TUNING APPLICATION-SPECIFIC MACHINE LEARNING ACCELERATORS simplified abstract (GOOGLE LLC)

GENERATING AND GLOBALLY TUNING APPLICATION-SPECIFIC MACHINE LEARNING ACCELERATORS

Organization Name

GOOGLE LLC

Inventor(s)

Yang Yang of Mountain View CA (US)

Claudionor Jose Nunes Coelho, Jr. of Redwood City CA (US)

Hao Zhuang of San Jose CA (US)

Aki Oskari Kuusela of Palo Alto CA (US)

GENERATING AND GLOBALLY TUNING APPLICATION-SPECIFIC MACHINE LEARNING ACCELERATORS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18289292 titled 'GENERATING AND GLOBALLY TUNING APPLICATION-SPECIFIC MACHINE LEARNING ACCELERATORS

The patent application describes methods, systems, and apparatus for globally tuning and generating ML hardware accelerators.

• A design system selects a baseline processor configuration architecture.
• An ML cost model generates performance data by modeling how the architecture executes computations of a neural network with multiple layers.
• The architecture is dynamically tuned based on the performance data to meet a performance objective when implementing the neural network for a target application.
• In response to tuning the architecture, a configuration of an ML accelerator is generated with customized hardware configurations for each layer of the neural network.

1. 1. 1. Key Features and Innovation:

• Selection of a baseline processor configuration architecture.
• Generation of performance data through an ML cost model.
• Dynamic tuning of the architecture to meet performance objectives.
• Generation of customized hardware configurations for each layer of the neural network.

1. 1. 1. Potential Applications:

This technology can be applied in various industries such as artificial intelligence, machine learning, data processing, and computer hardware development.

1. 1. 1. Problems Solved:

This technology addresses the need for efficient and optimized hardware accelerators for machine learning applications.

1. 1. 1. Benefits:

• Improved performance and efficiency in executing machine learning computations.
• Customized hardware configurations for specific neural network layers.
• Enhanced capabilities for implementing machine learning algorithms.

1. 1. 1. Commercial Applications:

The technology can be utilized in developing advanced machine learning systems, data centers, cloud computing, and AI-driven applications, leading to improved performance and efficiency.

1. 1. 1. Prior Art:

Readers can explore prior research in the fields of machine learning hardware accelerators, neural network optimization, and performance tuning in computer systems.

1. 1. 1. Frequently Updated Research:

Stay updated on the latest advancements in machine learning hardware accelerators, neural network optimization, and performance tuning in computer systems.

1. 1. 1. 1. Questions about ML Hardware Accelerators:

1. What are the key components of a hardware accelerator for machine learning applications? 2. How does dynamic tuning of hardware accelerators improve performance in neural network computations?

Original Abstract Submitted

Methods, systems, and apparatus, including computer-readable media, are described for globally tuning and generating ML hardware accelerators. A design system selects an architecture representing a baseline processor configuration. An ML cost model of the system generates performance data about the architecture at least by modeling how the architecture executes computations of a neural network that includes multiple layers. Based on the performance data, the architecture is dynamically tuned to satisfy a performance objective when the architecture implements the neural network and executes machine-learning computations for a target application. In response to dynamically tuning the architecture, the system generates a configuration of an ML accelerator that specifies customized hardware configurations for implementing each of the multiple layers of the neural network.