View source for 18456235. COMPUTE OPTIMIZATIONS FOR LOW PRECISION MACHINE LEARNING OPERATIONS simplified abstract (Intel Corporation)

=COMPUTE OPTIMIZATIONS FOR LOW PRECISION MACHINE LEARNING OPERATIONS=


==Organization Name==

[[:Category:Intel Corporation|Intel Corporation]]


[[Category:Intel Corporation]]

==Inventor(s)==

[[:Category:Elmoustapha Ould-ahmed-vall of Chandler AZ (US)|Elmoustapha Ould-ahmed-vall of Chandler AZ (US)]][[Category:Elmoustapha Ould-ahmed-vall of Chandler AZ (US)]]

[[:Category:Sara S. Baghsorkhi of San Jose CA (US)|Sara S. Baghsorkhi of San Jose CA (US)]][[Category:Sara S. Baghsorkhi of San Jose CA (US)]]

[[:Category:Anbang Yao of Beijing (CN)|Anbang Yao of Beijing (CN)]][[Category:Anbang Yao of Beijing (CN)]]

[[:Category:Kevin Nealis of San Jose CA (US)|Kevin Nealis of San Jose CA (US)]][[Category:Kevin Nealis of San Jose CA (US)]]

[[:Category:Xiaoming Chen of Shanghai (CN)|Xiaoming Chen of Shanghai (CN)]][[Category:Xiaoming Chen of Shanghai (CN)]]

[[:Category:Altug Koker of El Dorado Hills CA (US)|Altug Koker of El Dorado Hills CA (US)]][[Category:Altug Koker of El Dorado Hills CA (US)]]

[[:Category:Abhishek R. Appu of El Dorado Hills CA (US)|Abhishek R. Appu of El Dorado Hills CA (US)]][[Category:Abhishek R. Appu of El Dorado Hills CA (US)]]

[[:Category:John C. Weast of Portland OR (US)|John C. Weast of Portland OR (US)]][[Category:John C. Weast of Portland OR (US)]]

[[:Category:Mike B. Macpherson of Portland OR (US)|Mike B. Macpherson of Portland OR (US)]][[Category:Mike B. Macpherson of Portland OR (US)]]

[[:Category:Dukhwan Kim of San Jose CA (US)|Dukhwan Kim of San Jose CA (US)]][[Category:Dukhwan Kim of San Jose CA (US)]]

[[:Category:Linda L. Hurd of Cool CA (US)|Linda L. Hurd of Cool CA (US)]][[Category:Linda L. Hurd of Cool CA (US)]]

[[:Category:Ben J. Ashbaugh of Folsom CA (US)|Ben J. Ashbaugh of Folsom CA (US)]][[Category:Ben J. Ashbaugh of Folsom CA (US)]]

[[:Category:Barath Lakshmanan of Chandler AZ (US)|Barath Lakshmanan of Chandler AZ (US)]][[Category:Barath Lakshmanan of Chandler AZ (US)]]

[[:Category:Liwei Ma of Beijing (CN)|Liwei Ma of Beijing (CN)]][[Category:Liwei Ma of Beijing (CN)]]

[[:Category:Joydeep Ray of Folsom CA (US)|Joydeep Ray of Folsom CA (US)]][[Category:Joydeep Ray of Folsom CA (US)]]

[[:Category:Ping T. Tang of Edison NJ (US)|Ping T. Tang of Edison NJ (US)]][[Category:Ping T. Tang of Edison NJ (US)]]

[[:Category:Michael S. Strickland of Sunnyvale CA (US)|Michael S. Strickland of Sunnyvale CA (US)]][[Category:Michael S. Strickland of Sunnyvale CA (US)]]

==COMPUTE OPTIMIZATIONS FOR LOW PRECISION MACHINE LEARNING OPERATIONS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18456235 titled 'COMPUTE OPTIMIZATIONS FOR LOW PRECISION MACHINE LEARNING OPERATIONS

==Simplified Explanation==

The abstract of the patent application describes a general-purpose graphics processing unit (GPU) that includes a dynamic precision floating-point unit. This unit has a control unit with precision tracking hardware logic to monitor the number of bits of precision for computed data in relation to a target precision. The dynamic precision floating-point unit also has computational logic to output data at multiple precisions.

* The patent application describes a general-purpose GPU with a dynamic precision floating-point unit.
* The dynamic precision floating-point unit includes a control unit with precision tracking hardware logic.
* The precision tracking hardware logic monitors the number of bits of precision for computed data.
* The precision tracking hardware logic compares the computed data precision to a target precision.
* The dynamic precision floating-point unit also includes computational logic to output data at multiple precisions.

Potential applications of this technology:

* Graphics processing: The dynamic precision floating-point unit can enhance the performance and efficiency of graphics processing in GPUs.
* Scientific computing: The ability to output data at multiple precisions can be beneficial for scientific simulations and calculations.
* Machine learning: GPUs are commonly used in machine learning applications, and the dynamic precision floating-point unit can improve the accuracy and efficiency of these computations.

Problems solved by this technology:

* Precision optimization: The precision tracking hardware logic helps optimize the precision of computed data, ensuring it meets the desired target precision without unnecessary over-precision.
* Performance improvement: By outputting data at multiple precisions, the dynamic precision floating-point unit can improve the overall performance of the GPU, especially in scenarios where different levels of precision are required.

Benefits of this technology:

* Improved efficiency: The dynamic precision floating-point unit allows for more efficient use of computational resources by dynamically adjusting the precision of computed data.
* Enhanced accuracy: The precision tracking hardware logic ensures that computed data meets the desired precision, resulting in more accurate calculations.
* Versatility: The ability to output data at multiple precisions makes the GPU suitable for a wide range of applications that require different levels of precision.


==Original Abstract Submitted==

One embodiment provides a general-purpose graphics processing unit comprising a dynamic precision floating-point unit including a control unit having precision tracking hardware logic to track an available number of bits of precision for computed data relative to a target precision, wherein the dynamic precision floating-point unit includes computational logic to output data at multiple precisions.



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