Fixed-Point Code Synthesis For Neural Networks

• URL: http://arxiv.org/abs/2202.02095v1
• Date: Fri, 4 Feb 2022 12:02:54 GMT
• Title: Fixed-Point Code Synthesis For Neural Networks
• Authors: Hanane Benmaghnia, Matthieu Martel and Yassamine Seladji
• Abstract summary: A new technique is introduced to tune the formats (precision) of already trained neural networks using fixed-point arithmetic. The new optimized neural network computes the output with fixed-point numbers without modifying the accuracy up to a threshold fixed by the user.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over the last few years, neural networks have started penetrating safety critical systems to take decisions in robots, rockets, autonomous driving car, etc. A problem is that these critical systems often have limited computing resources. Often, they use the fixed-point arithmetic for its many advantages (rapidity, compatibility with small memory devices.) In this article, a new technique is introduced to tune the formats (precision) of already trained neural networks using fixed-point arithmetic, which can be implemented using integer operations only. The new optimized neural network computes the output with fixed-point numbers without modifying the accuracy up to a threshold fixed by the user. A fixed-point code is synthesized for the new optimized neural network ensuring the respect of the threshold for any input vector belonging the range [xmin, xmax] determined during the analysis. From a technical point of view, we do a preliminary analysis of our floating neural network to determine the worst cases, then we generate a system of linear constraints among integer variables that we can solve by linear programming. The solution of this system is the new fixed-point format of each neuron. The experimental results obtained show the efficiency of our method which can ensure that the new fixed-point neural network has the same behavior as the initial floating-point neural network.

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