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All Journal JURNAL NASIONAL TEKNIK ELEKTRO Community Empowerment
Infall Syafalni
Institut Teknologi Bandung
Arts Humanities Education Electrical & Electronics Engineering Public Health Social Sciences

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Optimized Multiple-Bit-Flip Soft-Errors-Tolerant TCAM using Machine Learning Infall Syafalni; Trio Adiono
JURNAL NASIONAL TEKNIK ELEKTRO Vol 11, No 1: March 2022
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1057.833 KB) | DOI: 10.25077/jnte.v11n1.1007.2022

Abstract

Soft errors from radiations can change the data in electronic devices especially memory cells such as in TCAMs. The soft errors cause bit-flip errors that makes the data are corrupted in the network. This paper presents a novel machine learning for a multiple-bit-flip-tolerant TCAM that address soft errors problem using partial don't-care keys (X-keys). The general methodology is classified into two steps, i.e., statistical training and X-keys matching. First, we train the machine by collecting match probability of a filter by using X-keys that match the same locations as the search key. This method uses statistical training to determine the most efficient of number of don't cares. Moreover, in the statistical training, we also explore the maximum number of don't cares that produce best performance in covering the soft errors. Finally, the X-keys are implemented in the TCAM to correct bit-flip errors. The suitable number of don't cares in X-key is determined from the distribution of match probability of the X-keys so that the best degree of tolerance of the TCAM against soft errors is found. Match probabilities for various filters are shown. Experimental results demonstrate that the soft-error tolerance using statistical data has better soft-error tolerance than other methods. The proposed method is useful for soft-error tolerant TCAMs in routers and firewalls for robust networks.
Transforming aquaculture monitoring with real-time solutions at Salman Assalam Science Islamic Boarding School, Cirebon Trio Adiono; Syifaul Fuada; Infall Syafalni; Feiza Alfi; Imran Abdurrahman; Sandi Pamungkas; Najma Khansa Alya Afandi; Leonardi Paris Hasugian
Community Empowerment Vol 9 No 1 (2024)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ce.10474

Abstract

The aim of this activity is to disseminate real-time, 24-hour aquaculture water monitoring devices remotely at the Salman Assalam Science Islamic Boarding School in Cirebon, West Java. The Salman Assalam Science Islamic Boarding School manages various types of tilapia, catfish, and pomfret cultivation ponds used to support the economy and fulfill the food needs of the students. The activities include planning, implementation, and evaluation. The results of this activity include the development of a laboratory-level device, comprising a buoy, solar panels, and electronic modules with several types of electronic sensors. The device is equipped with a dashboard to observe real-time measurements in fish ponds, including parameters such as water temperature, dissolved oxygen, turbidity, pH, air temperature, humidity, battery voltage, total dissolved density, and solar cell voltage. Data is presented in the form of graphs and tables accessible via a web browser, allowing partners to access it from laptops or smartphones.
Co-Authors Feiza Alfi Imran Abdurrahman Leonardi Paris Hasugian Najma Khansa Alya Afandi Sandi Pamungkas Syifaul Fuada Trio Adiono