Neural Network Processor for Product Recommendation Speed
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Solution Overview
Problem
General-purpose processors face limitations in processing speed and efficiency, particularly when handling large loads, leading to delayed information retrieval in practical applications.
Innovation Solution
A computation device with a communication unit and operation unit, utilizing a neural network model with customizable functional layers, performs feature extraction on user data to provide product recommendations, optimizing processing speed and efficiency by reducing intermediate data storage and retrieval operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a general-purpose processor is used to obtain and process information by running a software program, then the system can perform product recommendation functions, but the processing speed is limited and the efficiency is low when the processor has a large load
Solution Approach 1:
The patent replaces the general-purpose processor running software programs with a neural network processor that executes neural network operations directly. This substitution of the processing mechanism enables parallel computation of neural network layers, significantly improving processing speed and reducing delay in information retrieval for product recommendation functions.
Solution Approach 2:
The neural network processor divides the processing task into multiple functional layers (input layer, hidden layers, output layer), where each layer can be processed independently and in parallel. This segmentation of the computation into discrete, parallelizable operations enables the system to handle large loads efficiently while maintaining high processing speed.
2Productivity
If a neural network processor is used to process user data through functional layers, then processing speed and efficiency are improved, but the device structure becomes more complex
Solution Approach 1:
The neural network processor is designed with multi-functionality, where the same hardware structure can execute different neural network operations (convolution, pooling, activation, fully connected layers) through configurable functional layers. This universality allows the device to handle various product recommendation tasks without requiring separate specialized hardware for each operation, thus managing complexity while maintaining high processing efficiency.
Data Source
AI summary
Disclosed are an information processing method and a terminal device. The method comprises: acquiring first information, wherein the first information is information to be processed by a terminal device, calling an operation instruction in a calculation apparatus to calculate the first information so as to obtain second information, and outputting the second information. By means of the examples in the present disclosure, a calculation apparatus of a terminal device can be used to call an operation instruction to process first information, so as to output second information of a target desired by a user, thereby improving the information processing efficiency. The present technical solution has advantages of a fast computation speed and high efficiency.