Neural Network Data Representation for Processing Efficiency
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Solution Overview
Problem
The increasing volume of data, particularly image data, exceeds the processing capabilities of conventional processors and techniques, making it intractable for efficient analysis and requiring innovative hardware architectures and software techniques.
Innovation Solution
The use of specialized data representation in neural networks, specifically converting 8-bit unsigned integer image data to a 16-bit reduced floating-point representation like bfloat16, allows for reduced computational complexity, faster processing, and improved convergence without loss of precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional processors and techniques are used to analyze large volumes of data, then data processing capability is maintained at standard levels, but processing efficiency and scalability become intractable
Solution Approach 1:
The patent changes the data representation parameters from conventional formats to a specialized format optimized for neural network operations. This involves transforming how data is stored and processed, using specific bit-width configurations and numerical representations that align with neural network computational requirements, thereby improving processing efficiency without proportionally increasing system complexity
Solution Approach 2:
The patent creates a universal data representation format that can be used across different neural network operations and layers. This specialized format serves multiple functions including storage, transmission, and computation within the neural network pipeline, eliminating the need for separate conventional processing paths and improving overall system scalability
2Quantity of substance
If data is stored and processed in conventional formats, then compatibility with standard processors is maintained, but storage and computation requirements become excessive
Solution Approach 1:
The patent applies parameter changes by transitioning from conventional data formats to a specialized representation that optimizes the balance between storage efficiency and precision. This involves selecting specific numerical formats with appropriate bit allocations that reduce storage requirements while maintaining the precision necessary for accurate neural network computations
3Speed
If standard processing techniques are used, then implementation simplicity is maintained, but processing speed and convergence rates remain slow
Solution Approach 1:
The patent changes processing parameters by adopting a specialized data representation that enables faster computational operations. This includes using data formats that are optimized for parallel processing and neural network-specific operations, thereby increasing processing speed and convergence rates while managing architecture complexity through targeted optimizations
Data Source
AI summary
Techniques for neural network processing using specialized data representation are disclosed. Input data for manipulation in a layer of a neural network is obtained. The input data includes image data, where the image data is represented in bfloat16 format without loss of precision. The manipulation of the input data is performed on a processor that supports single-precision operations. The input data is converted to a 16-bit reduced floating-point representation, where the reduced floating-point representation comprises an alternative single-precision data representation mode. The input data is manipulated with one or more 16-bit reduced floating-point data elements. The manipulation includes a multiply and add-accumulate operation. The manipulation further includes a unary operation, a binary operation, or a conversion operation. A result of the manipulating is forwarded to a next layer of the neural network.