View source for 18444249. LOW LATENCY MATRIX MULTIPLY UNIT simplified abstract (Google LLC)

=LOW LATENCY MATRIX MULTIPLY UNIT=


==Organization Name==

[[:Category:Google LLC|Google LLC]]


[[Category:Google LLC]]

==Inventor(s)==

[[:Category:Andrew Everett Phelps of Middleton WI (US)|Andrew Everett Phelps of Middleton WI (US)]][[Category:Andrew Everett Phelps of Middleton WI (US)]]

[[:Category:Norman Paul Jouppi of Palo Alto CA (US)|Norman Paul Jouppi of Palo Alto CA (US)]][[Category:Norman Paul Jouppi of Palo Alto CA (US)]]

==LOW LATENCY MATRIX MULTIPLY UNIT - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18444249 titled 'LOW LATENCY MATRIX MULTIPLY UNIT

'''Simplified Explanation:'''
The patent application describes a matrix multiply unit implemented as a systolic array of cells. Each cell includes various components for processing weight inputs and vector data inputs to perform matrix multiplication efficiently.

* A weight matrix register receives weight inputs from transposed or non-transposed weight shift registers.
* A transposed weight shift register stores weight inputs from a horizontal direction.
* A non-transposed weight shift register stores weight inputs from a vertical direction.
* A multiply unit multiplies the weight input with a vector data input to produce a multiplication result.

'''Key Features and Innovation:'''
* Systolic array structure for matrix multiplication.
* Efficient processing of weight and vector data inputs.
* Utilization of transposed and non-transposed weight shift registers.
* High-performance multiply unit for accurate multiplication results.

'''Potential Applications:'''
The technology can be applied in:
* Machine learning algorithms.
* Signal processing systems.
* Image and video processing applications.
* Neural network architectures.

'''Problems Solved:'''
* Enhances the speed and efficiency of matrix multiplication.
* Improves accuracy in processing weight and vector data inputs.
* Enables complex mathematical operations in real-time applications.

'''Benefits:'''
* Faster computation of matrix multiplication tasks.
* Enhanced performance in various data processing applications.
* Improved accuracy and reliability in mathematical calculations.
* Scalable architecture for handling large datasets.

'''Commercial Applications:'''
Potential commercial uses include:
* AI and machine learning systems.
* Data analytics platforms.
* Cloud computing services.
* High-performance computing solutions.

'''Prior Art:'''
Readers can explore prior research on systolic arrays, matrix multiplication units, and parallel computing architectures to understand the background of this technology.

'''Frequently Updated Research:'''
Stay updated on advancements in systolic array designs, matrix multiplication algorithms, and parallel processing techniques to enhance the efficiency and performance of similar technologies.

'''Questions about Matrix Multiply Unit:'''
1. How does the systolic array structure improve the efficiency of matrix multiplication?
2. What are the key components of each cell in the matrix multiply unit and how do they contribute to the overall functionality?


==Original Abstract Submitted==

Methods, systems, and apparatus for a matrix multiply unit implemented as a systolic array of cells are disclosed. Each cell of the matrix multiply includes: a weight matrix register configured to receive a weight input from either a transposed or a non-transposed weight shift register; a transposed weight shift register configured to receive a weight input from a horizontal direction to be stored in the weight matrix register; a non-transposed weight shift register configured to receive a weight input from a vertical direction to be stored in the weight matrix register; and a multiply unit that is coupled to the weight matrix register and configured to multiply the weight input of the weight matrix register with a vector data input in order to obtain a multiplication result.



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