D. Godoy, S. Xia, Wendy P. Fernandez, Xiaofan Jiang, P. Kinget
{"title":"摘要:基于定制集成电路的超低功耗声源定位系统","authors":"D. Godoy, S. Xia, Wendy P. Fernandez, Xiaofan Jiang, P. Kinget","doi":"10.1109/IoTDI.2018.00056","DOIUrl":null,"url":null,"abstract":"The aim of this demo is to explore the implementation of an ultra-low-power analog-to-feature ASIC to an IoT embedded system. The custom integrated circuit, designed to optimize the power consumption of a traditional sound-source localization system, is capable of extracting the time-difference of arrival (TDoA) between 4 microphones consuming only 78.2nW. An end-to-end embedded system is presented; a microphone array is connected to the ASIC that converts the TDoA to digital information and sends it to a host computer. A machine-learning algorithm, running in the host, is then used to detect the bearing of the sound source. During the demonstration, the audience is able to verify the benefits and drawbacks of the custom integrated circuit solution, both in the perspective of the signal-processing performance of the ASIC, and the impact it introduces to the complexity of the system's integration.","PeriodicalId":149725,"journal":{"name":"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Demo Abstract: An Ultra-Low-Power Custom Integrated Circuit Based Sound-Source Localization System\",\"authors\":\"D. Godoy, S. Xia, Wendy P. Fernandez, Xiaofan Jiang, P. Kinget\",\"doi\":\"10.1109/IoTDI.2018.00056\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The aim of this demo is to explore the implementation of an ultra-low-power analog-to-feature ASIC to an IoT embedded system. The custom integrated circuit, designed to optimize the power consumption of a traditional sound-source localization system, is capable of extracting the time-difference of arrival (TDoA) between 4 microphones consuming only 78.2nW. An end-to-end embedded system is presented; a microphone array is connected to the ASIC that converts the TDoA to digital information and sends it to a host computer. A machine-learning algorithm, running in the host, is then used to detect the bearing of the sound source. During the demonstration, the audience is able to verify the benefits and drawbacks of the custom integrated circuit solution, both in the perspective of the signal-processing performance of the ASIC, and the impact it introduces to the complexity of the system's integration.\",\"PeriodicalId\":149725,\"journal\":{\"name\":\"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IoTDI.2018.00056\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IoTDI.2018.00056","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Demo Abstract: An Ultra-Low-Power Custom Integrated Circuit Based Sound-Source Localization System
The aim of this demo is to explore the implementation of an ultra-low-power analog-to-feature ASIC to an IoT embedded system. The custom integrated circuit, designed to optimize the power consumption of a traditional sound-source localization system, is capable of extracting the time-difference of arrival (TDoA) between 4 microphones consuming only 78.2nW. An end-to-end embedded system is presented; a microphone array is connected to the ASIC that converts the TDoA to digital information and sends it to a host computer. A machine-learning algorithm, running in the host, is then used to detect the bearing of the sound source. During the demonstration, the audience is able to verify the benefits and drawbacks of the custom integrated circuit solution, both in the perspective of the signal-processing performance of the ASIC, and the impact it introduces to the complexity of the system's integration.