Characterization of Neural Networks Automatically Mapped on Automotive-grade Microcontrollers

• URL: http://arxiv.org/abs/2103.00201v1
• Date: Sat, 27 Feb 2021 12:16:50 GMT
• Title: Characterization of Neural Networks Automatically Mapped on Automotive-grade Microcontrollers
• Authors: Giulia Crocioni, Giambattista Gruosso, Danilo Pau, Davide Denaro, Luigi Zambrano, Giuseppe di Giore
• Abstract summary: We present a framework for implementing Neural Network-based models on a family of automotive Microcontrollers. In this paper, we show their efficiency in two case studies applied to vehicles: intrusion detection on the Controller Area Network bus and residual capacity estimation in Lithium-Ion batteries.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Nowadays, Neural Networks represent a major expectation for the realization of powerful Deep Learning algorithms, which can determine several physical systems' behaviors and operations. Computational resources required for model, training, and running are large, especially when related to the amount of data that Neural Networks typically need to generalize. The latest TinyML technologies allow integrating pre-trained models on embedded systems, allowing making computing at the edge faster, cheaper, and safer. Although these technologies originated in the consumer and industrial worlds, many sectors can greatly benefit from them, such as the automotive industry. In this paper, we present a framework for implementing Neural Network-based models on a family of automotive Microcontrollers, showing their efficiency in two case studies applied to vehicles: intrusion detection on the Controller Area Network bus and residual capacity estimation in Lithium-Ion batteries, widely used in Electric Vehicles.

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