View source for 17813834. HYBRID MACHINE LEARNING ARCHITECTURE WITH NEURAL PROCESSING UNIT AND COMPUTE-IN-MEMORY PROCESSING ELEMENTS simplified abstract (QUALCOMM Incorporated)

=HYBRID MACHINE LEARNING ARCHITECTURE WITH NEURAL PROCESSING UNIT AND COMPUTE-IN-MEMORY PROCESSING ELEMENTS=


==Organization Name==

[[:Category:QUALCOMM Incorporated|QUALCOMM Incorporated]]


[[Category:QUALCOMM Incorporated]]

==Inventor(s)==

[[:Category:Mustafa Badaroglu of Leuven (BE)|Mustafa Badaroglu of Leuven (BE)]][[Category:Mustafa Badaroglu of Leuven (BE)]]

[[:Category:Zhongze Wang of San Diego CA (US)|Zhongze Wang of San Diego CA (US)]][[Category:Zhongze Wang of San Diego CA (US)]]

[[:Category:Titash Rakshit of Austin TX (US)|Titash Rakshit of Austin TX (US)]][[Category:Titash Rakshit of Austin TX (US)]]

==HYBRID MACHINE LEARNING ARCHITECTURE WITH NEURAL PROCESSING UNIT AND COMPUTE-IN-MEMORY PROCESSING ELEMENTS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17813834 titled 'HYBRID MACHINE LEARNING ARCHITECTURE WITH NEURAL PROCESSING UNIT AND COMPUTE-IN-MEMORY PROCESSING ELEMENTS

==Simplified Explanation==

The patent application describes a hybrid architecture that combines neural processing units (NPU) and compute-in-memory (CIM) elements for performing machine learning tasks. This architecture includes a neural-network-processing circuit with CIM processing elements, NPU processing elements, and a bus connecting them.

* The architecture combines NPU and CIM elements for machine learning tasks.
* The neural-network-processing circuit includes CIM processing elements, NPU processing elements, and a bus.
* Data is processed in the circuit by transferring it between CIM and NPU processing elements via the bus.

== Potential Applications ==
* Machine learning tasks
* Artificial intelligence applications
* Data analysis and pattern recognition

== Problems Solved ==
* Improved performance and efficiency in machine learning tasks
* Enhanced processing capabilities for complex data analysis
* Integration of NPU and CIM elements for optimized machine learning algorithms

== Benefits ==
* Faster and more efficient machine learning processing
* Improved accuracy and performance in data analysis
* Enhanced capabilities for AI applications
* Potential for reduced power consumption and cost in machine learning systems


==Original Abstract Submitted==

Methods and apparatus for performing machine learning tasks, and in particular, a hybrid architecture that includes both neural processing unit (NPU) and compute-in-memory (CIM) elements. One example neural-network-processing circuit generally includes a plurality of CIM processing elements (PEs), a plurality of neural processing unit (NPU) PEs, and a bus coupled to the plurality of CIM PEs and to the plurality of NPU PEs. One example method for neural network processing generally includes processing data in a neural-network-processing circuit comprising a plurality of CIM PEs, a plurality of NPU PEs, and a bus coupled to the plurality of CIM PEs and to the plurality of NPU PEs; and transferring the processed data between at least one of the plurality of CIM PEs and at least one of the plurality of NPU PEs via the bus.



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