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2 results for “On-chip sensors”
MOSID (Microcontroller On-chip Sensor IDentification): A dataset of readings from the internal monitoring sensors of STM32L152RTXX microcontrollers during the stimulation of their electronic activity
<p>The MOSID (Microcontroller On-chip Sensor IDentification) dataset consists of 5 acquired data subsets (6,72 GB total, compressed into 560 MB), each collected during different experiments and periods using various equipment (HMP4040, DF1731SB & HM305) and acquisition strategies. These subsets contain readings from the temperature and voltage sensors embedded in 20 STM32L-DISCOVERY devices. The data was captured during the execution of 5 different workloads as stimuli, repeated over 20 iterations. The stimuli employed are as follows:</p><ol><li>20x20 Long-type matrix product.</li><li>20x20 Float-type matrix product.</li><li>Algorithm for ascending sorting, Bubble Sort.</li><li>Algorithm for 2D-point clustering, Convex Hull.</li><li>Encryption algorithm AES 128-bit.</li></ol><p>The subsets are structured according to the folder format "X_Y," where X is the manually assigned number to the board, and Y is the corresponding number for the executed algorithm. Within each of these folders, files are present in the format "data_Z.txt," where Z represents the iteration number to which the file belongs. In total, the dataset comprises 9600 files with a final size of approximately 7 GB. The different presented subsets are as follows:</p><ul><li>ACQ1: Derived from the experiment named "Automatic Acquisition 1 (HMP4040)" conducted using a daisy-chain topology (20 out of 20 boards, 2000 files).</li><li>ACQ2: Derived from the experiment named "Automatic Acquisition 2 (HMP4040)" conducted using a daisy-chain topology (20 out of 20 boards, 2000 files).</li><li>ACQ3: Derived from the experiment named "Individual Acquisitions (HMP4040)", performed board by board from idle conditions (20 out of 20 boards, 2000 files).</li><li>ACQ4: Derived from the experiment named "GOLD SOURCE DF1731SB Acquisitions" conducted using a partial daisy-chain setup (2 devices at a time, 18 out of 20 boards excluding boards , 1800 files).</li><li>ACQ5: Derived from the experiment named "HANMATEK HM305 Acquisitions" conducted using a partial daisy-chain setup (2 devices at a time, 18 out of 20 boards, 1800 files).</li></ul><p>In each "data_Z.txt" file, starting from the 5th line, temperature and voltage raw ADC conversions from the sensors are provided, captured during the execution of the stimulus in successive lines. Additionally, a table (Table_UIDS.csv) with metadata for each of the boards used in the experiments is included, which is needed in order to normalize the data in terms of ºC and Volts.</p><ul><li>BOARD_NUM, which contains the manually assigned board number.</li><li>UID, which contains the Unique Identifier of the board assigned by the manufacturer.</li><li>T_CAL_1, which holds the calibration value of the board's temperature sensor at 30ºC.</li><li>T_CAL_2, which holds the calibration value of the board's temperature sensor at 100ºC.</li><li>VREFINT_CAL, which contains the calibration value of the board's voltage sensor.</li></ul>
PS-BBICS: Pulse Stretching Bulk Built-in Current Sensor for On-chip Measurement of Single Event Transients (raw data from journal article)
<p>This upload contains raw data from the manuscript "PS-BBICS: Pulse Stretching Bulk Built-in Current Sensor for On-chip Measurement of Single Event Transients". The manuscript was published in Microelectronics Reliability, vol. 138, 2022.; DOI: https://doi.org/10.1016/j.microrel.2022.114726</p> <p>The upload consists of .pdf file of the manuscript and .opj files with raw data related to the figures in the manuscript. </p> <p>This work was supported in part by the European Union’s Horizon 2020 research and innovation programme (Grant No. 857558).</p>
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