View source for Apple inc. (20240273335). CUSTOMIZABLE CHIP FOR AI APPLICATIONS simplified abstract

=CUSTOMIZABLE CHIP FOR AI APPLICATIONS=


==Organization Name==

[[:Category:apple inc.|apple inc.]]


[[Category:apple inc.]]

==Inventor(s)==

[[:Category:Saman Naderiparizi of Seattle WA (US)|Saman Naderiparizi of Seattle WA (US)]][[Category:Saman Naderiparizi of Seattle WA (US)]]

[[:Category:Mohammad Rastegari of Bothell WA (US)|Mohammad Rastegari of Bothell WA (US)]][[Category:Mohammad Rastegari of Bothell WA (US)]]

[[:Category:Sayyed Karen Khatamifard of Seattle WA (US)|Sayyed Karen Khatamifard of Seattle WA (US)]][[Category:Sayyed Karen Khatamifard of Seattle WA (US)]]

==CUSTOMIZABLE CHIP FOR AI APPLICATIONS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240273335 titled 'CUSTOMIZABLE CHIP FOR AI APPLICATIONS

'''Simplified Explanation:'''
This patent application describes a computing device that includes a programmable logic device implementing a convolutional neural network, where each compute block of the device corresponds to a convolutional layer of the network.

'''Key Features and Innovation:'''
* Computing device with a programmable logic device implementing a convolutional neural network.
* Each compute block corresponds to a convolutional layer of the network.
* Memory blocks store data and parameters for each layer of the network.

'''Potential Applications:'''
This technology can be used in various fields such as image recognition, natural language processing, and autonomous vehicles.

'''Problems Solved:'''
This technology addresses the need for efficient and optimized implementation of convolutional neural networks in computing devices.

'''Benefits:'''
* Improved performance and accuracy in tasks such as image recognition.
* Efficient use of resources in implementing convolutional neural networks.
* Flexibility to adapt to different network architectures.

'''Commercial Applications:'''
This technology has potential commercial applications in industries such as healthcare, security, and manufacturing where image recognition and data analysis are crucial.

'''Questions about Convolutional Neural Networks:'''
1. How does this technology improve the efficiency of implementing convolutional neural networks?
2. What are the key advantages of using a programmable logic device for implementing convolutional neural networks?

'''Frequently Updated Research:'''
Stay updated on the latest advancements in convolutional neural networks and their applications in various industries to leverage the full potential of this technology.


==Original Abstract Submitted==

in one embodiment, a computing device includes an input sensor providing an input data; a programmable logic device (pld) implementing a convolutional neural network (cnn), wherein: each compute block of the pld corresponds to one of a multiple of convolutional layers of the cnn, each compute block of the pld is placed in proximity to at least two memory blocks, a first one of the memory blocks serves as a buffer for the corresponding layer of the cnn, and a second one of the memory blocks stores model-specific parameters for the corresponding layer of the cnn.



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