Radar Sensing in Assisted Living: An Overview

This paper gives an overview of trends in radar sensing for assisted living. It focuses on signal processing and classification, looking at conventional approaches, deep learning and fusion techniques. The last section shows examples of classification in human activity recognition and medical applications, e.g. breathing disorder and sleep stages recognition

Power Estimation on FPGAs Based on Signal Information Propagation Through Digital Operators

International audienc

Dynamic power estimation based on switching activity propagation

International audienc

Statistical Information Propagation Across Operators for Dynamic Power Estimation on FPGAs

International audiencePerformance, cost and energy consumption are the main key features to evaluate any embedded system. Low power system is needed then power optimization process is required. To optimize the power dissipation of an embedded system we have to estimate the power consumption first. This paper presents a new power estimation approach at early design phases which is based on the decomposition of a digital system into basic operators. Each operator has its own model which estimates the switching activity and the power consumption. By interconnecting several operators, statistical information like switching activities and percentage of logic high is then propagated to enable a global power estimation of a given system. The approach consists in simply adding the individual power consumption of each operator. The methodology has been evaluated in a use-case. The preliminary results indicate a promising speedup of the design process with less than 8.0% of error comparing to the classical power estimation tools

Power Modeling on FPGA: A Neural Model for RT-Level Power Estimation

International audienceToday reducing power consumption is a major concern especially when it concerns small embedded devices. Power optimization is required all along the design flow but particularly in the first steps where it has the strongest impact. In this work, we propose new power models based on neural networks that predict the power consumed by digital operators implemented on Field Programmable Gate Arrays (FPGAs). These operators are interconnected and the statistical information of data patterns are propagated among them. The obtained results make an overall power estimation of a specific design possible. A comparison is performed to evaluate the accuracy of our power models against the estimations provided by the Xilinx Power Analyzer (XPA) tool. Our approach is verified at system-level where different processing systems are implemented. A mean absolute percentage error which is less than 8% is shown versus the Xilinx classic flow dedicated to power estimation