Characterization of Neural Networks Automatically Mapped on Automotive-grade Microcontrollers
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Capacity EstimationIntrusion DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
OpTC -- A Toolchain for Deployment of Neural Networks on AURIX TC3xx Microcontrollers
The AURIX 2xx and 3xx families of TriCore microcontrollers are widely used in the automotive industry and, recently, also in applications that involve machine learning tasks. Yet, these applications are mainly engineered…
Code GenerationConcept for an Automatic Annotation of Automotive Radar Data Using AI-segmented Aerial Camera Images
This paper presents an approach to automatically annotate automotive radar data with AI-segmented aerial camera images. For this, the images of an unmanned aerial vehicle (UAV) above a radar vehicle are panoptically segm…
PositionDemo Abstract: Capuchin: A Neural Network Model Generator for 16-bit Microcontrollers
Resource-optimized deep neural networks (DNNs) nowadays run on microcontrollers to perform a wide variety of audio, image and sensor data classification tasks. Despite comprehensive support for deep learning tools for 32…
Deep LearningEvaluation of Convolution Primitives for Embedded Neural Networks on 32-bit Microcontrollers
Deploying neural networks on constrained hardware platforms such as 32-bit microcontrollers is a challenging task because of the large memory, computing and energy requirements of their inference process. To tackle these…
An affordable hardware-aware neural architecture search for deploying convolutional neural networks on ultra-low-power computing platforms
Hardware-aware neural architecture search (HW-NAS) allows the integration of Convolutional Neural Networks (CNNs) in microcontrollers devices by automatically designing neural architectures that can fit prearranged hardw…
Neural Architecture Search