Hardwired MLP Calculation Core for Real-Time Engine Control Models
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Solution Overview
Problem
Existing data-based models, such as Gaussian process models, require high computing capacity to achieve sufficient accuracy and often fail to meet real-time calculation requirements in control devices for complex systems like internal combustion engines, necessitating a more efficient hardware-based solution.
Innovation Solution
A hard-wired model calculation unit specifically designed for multi-layer perceptron models with a variable number of neurons, utilizing a hardware computing core that calculates output variables using input variables, weighting matrices, and offset values, transforming results with activation functions, allowing for real-time calculations with reduced software load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data-based models with high number of support points are used to achieve sufficient modeling accuracy, then modeling precision is improved, but computing capacity requirement increases
Solution Approach 1:
The patent segments the data-based model into multiple localized basis functions centered at support points, allowing the global modeling task to be divided into independent local calculations. This segmentation enables the use of fewer support points while maintaining accuracy, as each basis function contributes only to its local region rather than requiring global computation across all support points.
Solution Approach 2:
The patent applies partial action by using a limited number of localized basis functions only where needed in the input space, rather than computing contributions from all support points for every prediction. This reduces the computing capacity requirement while maintaining sufficient modeling accuracy in the regions of interest.
2Measurement precision
If data-based models with high computing capacity are used to achieve sufficient accuracy, then modeling precision is improved, but real-time calculation capability deteriorates
Solution Approach 1:
By segmenting the model into localized basis functions, the patent enables parallel computation of independent local contributions, significantly improving real-time calculation capability while maintaining modeling accuracy. Each basis function can be evaluated independently and simultaneously.
Solution Approach 2:
The patent uses pre-computed basis function parameters that are copied and applied repeatedly for different input points, avoiding redundant calculations. This copying approach maintains accuracy while dramatically improving computation speed for real-time applications.
3Measurement precision
If a large number of support points are used in data-based models, then modeling accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex global model into simple localized basis functions, reducing device complexity while maintaining accuracy. Each segment is computationally simple but collectively they provide high-fidelity modeling through their localized contributions.
Data Source
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AI summary
The invention relates to a model calculation unit for calculating a neuron layer of a multi-layered perceptron model, comprising a hardwired calculation core formed in hardware for calculating a fixedly preset computing algorithm in coupled functional blocks. The calculation core is designed to calculate an output variable for each neuron in a neuron layer of a multi-layered perceptron model having a number of neurons, depending on one or more input variables of an input variable vector, on a weighting matrix with weighting factors and an offset value predefined for each neuron. A sum of the values of the input variables, which are weighted with a weighting factor determined by the neuron and the input variable, and the offset value predefined for the neuron is calculated for each neuron, and the result is transformed with an activation function to obtain the output variable for the neuron.