A controlled accuracy approximation scheme of the sigmoid function for artificial neuron implementation based on Taylor's theorem and the Lagrange form of the error is proposed. The main advantages of the proposed solution are two: it provides a systematic way to guarantee the required accuracy and it reuses the circuitry of the linear part of the neuron to compute the sigmoid function. The sigmoid derivative is also available for artificial neural networks with online learning capabilities.
In the present scenario of technological breakthroughs in the automotive industry, machine learning is greatly contributing to the development of safer and more comfortable vehicles. In particular, personalization of the driving experience using machine learning is an innovative trend that comprises the development of both customized driver assistance systems (DAS) and in-cabin comfort features. In this work, a versatile hardware/software platform for personalized driver assistance, using online sequential extreme learning machines (OS-ELM), is presented. The system, based on a programmable systemon-chip (SoC), is able to recognize the driver and personalize the behavior of the car. The platform provides high speed, small size, efficient power consumption, and true capability for real-time adaptation (i.e. on-chip selflearning). In addition, due to the plasticity and scalability of the OS-ELM algorithm and the programmable nature of the SoC, this solution is flexible enough to cope with the incremental changes that the new generation of vehicles are demanding. The implementation details of a system, suitable for current levels of driving automation, are provided.
In this paper we propose to apply the Dynamic Partial Reconfiguration (DPR) technology to embedded systems intended for Intelligent Environments. To reach this goal, we have developed a system based on a Field Programmable Gate Array (FPGA) in which high performance hardware modules can be reconfigured on-line according to the necessities of the system at each moment. Two different implementations have been carried out to measure the time required to reconfigure each module and also to measure the FPGA resources that can be saved if we keep configured only the modules that are required at each time. The Obtained results show how this technique offers advantages in cost, size and power when applied to embedded systems for intelligent environments.
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