Juan Sapriza, A. Arnaud, Bruno Bellini, Felipe Estévez, M. Miguez
{"title":"Smart devices and RFID: towards an Android-based information system in the cattle-yards","authors":"Juan Sapriza, A. Arnaud, Bruno Bellini, Felipe Estévez, M. Miguez","doi":"10.1109/LASCAS53948.2022.9789072","DOIUrl":"https://doi.org/10.1109/LASCAS53948.2022.9789072","url":null,"abstract":"The main change being observed in the use of Radio Frequency Identification (RFID) tools within the cattle industry is not related to the technology itself like hardware or protocols. Instead, is to incorporate RFID as part of the information system combined with a cattle database, not only to be compliant with mandatory traceability requirements, but also to increase productivity; the more on-site information the more efficiency to take decisions on individual animals. In this work, the evolution of a Low Frequency RFID (LF-RFID) stick reader named Baqueano from a simple traceability tool, towards the first Android RFID stick reader aimed at the cattle industry, is presented.","PeriodicalId":356481,"journal":{"name":"2022 IEEE 13th Latin America Symposium on Circuits and System (LASCAS)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124668498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lucas D. X. Ribeiro, R. Jacobi, F. Júnior, Jones Yudi da Silva, I.S. Silva
{"title":"Evaluating a Machine Learning-based Approach for Cache Configuration","authors":"Lucas D. X. Ribeiro, R. Jacobi, F. Júnior, Jones Yudi da Silva, I.S. Silva","doi":"10.1109/LASCAS53948.2022.9789040","DOIUrl":"https://doi.org/10.1109/LASCAS53948.2022.9789040","url":null,"abstract":"As the systems perform progressively complex tasks, the search for energy efficiency in computational systems is constantly increasing. The cache memory has a fundamental role in this issue. Through dynamic cache reconfiguration techniques, it is possible to obtain an optimal cache configuration that minimizes the impacts of energy losses. To achieve this goal, a precise selection of cache parameters plays a fundamental role. In this work, a machine learning-based approach is evaluated to predict the optimal cache configuration for different applications considering their dynamic instructions and a variety of cache parameters, followed by experiments showing that using a smaller set of application instructions it is already possible to obtain good classification results from the proposed model. The results show that the model obtains an accuracy of 96.19% using the complete set of RISC-V instructions and 96.33% accuracy using the memory instructions set, a more concise set of instructions that directly affect the cache power model, besides decreasing the model complexity.","PeriodicalId":356481,"journal":{"name":"2022 IEEE 13th Latin America Symposium on Circuits and System (LASCAS)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126155339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}