Softmax Calculator Lookup Table Generator for Neural Networks
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Solution Overview
Problem
Softmax calculations in neural networks require significant time and system resources due to exponential function calculations, necessitating a method for faster and more resource-efficient processing without compromising accuracy.
Innovation Solution
A digital signal processing device employs a lookup table generator to create tables for exponential functions, allowing the softmax calculator to read and use precomputed values, reducing the need for direct exponential calculations and minimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exponential function calculations are performed directly for softmax, then calculation accuracy is maintained, but processing time and system resource consumption increase significantly
Solution Approach 1:
The patent pre-calculates and stores exponential function values in lookup tables during system initialization or offline processing. During real-time inference, the system retrieves precomputed values from the lookup tables instead of performing expensive exponential calculations, thereby reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent creates a simplified representation of the exponential function through lookup tables that store precomputed values. This copy approach replaces the complex real-time exponential calculation with simple table lookup operations, significantly reducing computational burden and processing time.
2Measurement precision
If exponential function calculations are performed directly for softmax, then calculation accuracy is maintained, but system resource consumption increases
Solution Approach 1:
The patent performs expensive exponential calculations in advance during system initialization or offline processing, storing the results in lookup tables. During actual inference, the system only performs simple table lookups and basic arithmetic operations, dramatically reducing real-time resource consumption while preserving calculation accuracy.
Solution Approach 2:
The patent replaces complex exponential function evaluations with simplified table lookup operations. The lookup tables serve as a precomputed copy of the exponential function values, allowing the system to retrieve results without consuming resources for heavy computational operations during inference.
3Productivity
If lookup tables are used for exponential functions, then processing speed increases and resource consumption decreases, but device complexity increases due to table generation and storage requirements
Solution Approach 1:
The patent implements lookup table generation as a one-time preliminary operation performed during system initialization or offline processing. Once the tables are created, the actual inference process becomes simple and fast, with the complexity confined to the initialization phase rather than the ongoing operational phase.
Solution Approach 2:
The patent uses lookup tables as precomputed copies of exponential function values. This approach trades the complexity of generating and storing the tables during initialization for simplified, fast table lookup operations during inference, effectively moving the complexity burden from the operational phase to the setup phase.
Data Source
AI summary
A digital signal processing device is provided. The digital signal processing devices includes: a processor configured to execute instructions to implement: a lookup table generator configured to generate a first lookup table corresponding to a first exponential function, based on an input scaling value; and a softmax calculator configured to receive input data indicating input values, calculate a first index of the first lookup table, the first index corresponding to a first input value of the input values, read a first exponential function value corresponding to the first index from the first lookup table, calculate a first intermediate value based on the first exponential function value and the first input value, and generate output data indicating output values respectively corresponding to the input values, wherein a first output value of the output values is generated based on the first intermediate value.