Samsung electronics co., ltd. (20240119270). WEIGHT-SPARSE NPU WITH FINE-GRAINED STRUCTURED SPARSITY simplified abstract

WEIGHT-SPARSE NPU WITH FINE-GRAINED STRUCTURED SPARSITY

Organization Name

samsung electronics co., ltd.

Inventor(s)

Jong Hoon Shin of San Jose CA (US)

Ardavan Pedram of Santa Clara CA (US)

Joseph Hassoun of Los Gatos CA (US)

WEIGHT-SPARSE NPU WITH FINE-GRAINED STRUCTURED SPARSITY - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240119270 titled 'WEIGHT-SPARSE NPU WITH FINE-GRAINED STRUCTURED SPARSITY

Simplified Explanation

The patent application describes a neural processing unit that can be reconfigured to process a fine-grain structured sparsity weight arrangement selected from various options. The unit includes a weight buffer, a weight multiplexer array, an activation buffer, an activation multiplexer array, and a multiplier array to perform operations on weight and activation values based on the selected sparsity weight arrangement.

• Weight buffer stores weight values
• Weight multiplexer array outputs weight values based on selected sparsity arrangement
• Activation buffer stores activation values
• Activation multiplexer array outputs activation values based on selected sparsity arrangement
• Multiplier array calculates product values for operand value pairs

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1. 1. 1. Potential Applications

This technology can be applied in:

• Neural network processing
• Machine learning algorithms
• Image recognition systems

1. 1. 1. Problems Solved

This technology addresses:

• Efficient processing of sparse weight arrangements
• Reconfigurability for different sparsity patterns
• Optimization of neural network operations

1. 1. 1. Benefits

The benefits of this technology include:

• Improved performance in neural network computations
• Flexibility in handling various weight sparsity arrangements
• Enhanced efficiency in processing sparse data

1. 1. 1. Potential Commercial Applications

This technology can be utilized in:

• AI hardware development
• Cloud computing services
• Autonomous vehicles technology

1. 1. 1. Possible Prior Art

One possible prior art for this technology could be:

• Reconfigurable neural processing units with fixed weight arrangements

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1. 1. 1. 1. Unanswered Questions

1. 1. 1. 1. How does this technology compare to existing neural processing units in terms of performance and efficiency?

This article does not provide a direct comparison with existing neural processing units. Further research or testing may be needed to determine the specific advantages of this technology over current solutions.

1. 1. 1. 1. What are the potential limitations or drawbacks of implementing this technology in practical applications?

The article does not discuss any potential limitations or drawbacks of implementing this technology. Additional analysis or real-world testing may be necessary to identify any challenges that could arise when using this technology in various applications.

Original Abstract Submitted

a neural processing unit is reconfigurable to process a fine-grain structured sparsity weight arrangement selected from n:m=1:4, 2:4, 2:8 and 4:8 fine-grain structured weight sparsity arrangements. a weight buffer stores weight values and a weight multiplexer array outputs one or more weight values stored in the weight buffer as first operand values based on a selected fine-grain structured sparsity weight arrangement. an activation buffer stores activation values and an activation multiplexer array outputs one or more activation values stored in the activation buffer as second operand values based on the selected fine-grain structured weight sparsity in which each respective second operand value and a corresponding first operand value forms an operand value pair. a multiplier array outputs a product value for each operand value pair.