AI Accelerator LUT Interpolation for Activation Function Accuracy
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Solution Overview
Problem
Existing AI accelerators face challenges in efficiently approximating non-linear activation functions used in neural networks, requiring significant silicon area and compromising on accuracy and speed due to inefficient calculation methods.
Innovation Solution
A piece-wise approximation method using quadratic interpolation within non-uniform intervals and linear extrapolation for activation functions, implemented with a look-up table (LUT) in hardware, allowing for efficient and accurate computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a piece-wise approximation method with non-uniform intervals and quadratic interpolation is used, then accuracy of activation function approximation is improved, but device complexity increases due to LUT requirements
Solution Approach 1:
The input domain is divided into multiple non-uniform intervals, with each interval having its own quadratic interpolation parameters stored in separate LUT entries. This segmentation allows high accuracy within each interval while managing overall complexity through structured organization of parameters.
Solution Approach 2:
Different intervals are assigned different quadratic parameters (a, b, c) optimized for local characteristics of the activation function. The non-uniform interval distribution concentrates more intervals where the function has higher curvature, providing locally adapted approximation quality.
2Adaptability or versatility
If reprogrammable LUTs are implemented to support multiple activation functions, then adaptability is improved, but silicon area increases
Solution Approach 1:
A single LUT structure is designed to support multiple activation functions (sigmoid, tanh, swish, etc.) by storing parameters for different functions in different memory locations. The same hardware infrastructure can be reconfigured to approximate any supported activation function, eliminating the need for separate dedicated hardware for each function.
Solution Approach 2:
The LUT parameters are made reprogrammable, allowing the activation function type to be dynamically changed during operation. This enables the hardware accelerator to adapt to different neural network architectures and training requirements without physical reconfiguration.
3Measurement precision
If non-uniform intervals are used for quadratic interpolation, then approximation accuracy is improved, but calculation overhead increases
Solution Approach 1:
The quadratic parameters (a, b, c) for each non-uniform interval are pre-calculated and stored in the LUT during design time. During runtime, the system only needs to perform simple parameter retrieval and quadratic evaluation, avoiding complex real-time optimization calculations while maintaining high accuracy.
Data Source
AI summary
A method for approximating an activation function, the method including: receiving an input value of the activation function; determining that the input value is within a range, the range includes a set of non-uniform intervals; determining a selected interval from among the set of non-uniform intervals including the input value; retrieving, by a hardware accelerator, from a look-up table (LUT) associated with a type of the activation function, values of one or more quadratic interpolation parameters associated with the selected interval; performing a quadratic interpolation on the input value to approximate the input value using the values of the one or more quadratic interpolation parameters; and determining a first approximated output of the activation function based on a result of the quadratic interpolation performed on the input value.