BEGIN:VCALENDAR VERSION:2.0 PRODID:-//wp-events-plugin.com//7.2.3.1//EN TZID:Asia/Kolkata X-WR-TIMEZONE:Asia/Kolkata BEGIN:VEVENT UID:60@cds.iisc.ac.in DTSTART;TZID=Asia/Kolkata:20240705T145000 DTEND;TZID=Asia/Kolkata:20240705T155000 DTSTAMP:20240701T131408Z URL:https://cds.iisc.ac.in/events/ph-d-thesis-defense-online-mode-cds-05-j uly-2024data-driven-approach-to-estimate-wcet-for-real-time-systems/ SUMMARY:Ph.D: Thesis Defense: ONLINE MODE: CDS: 05\, July 2024"Data-Driven Approach to Estimate WCET for Real-Time Systems" DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nPh.D. Thesis Def ense\n\n\n\nSpeaker : Mr. Vikash Kumar\nS.R. Number : 06-18-02-10-12-18-1- 16344\nTitle : "Data-Driven Approach to Estimate WCET for Real-Time System s"\nResearch Supervisor : S K Nandy (retd) and S Raha\, CDS\nDate &\; T ime : July 05\, 2024 (Friday)\, 02:50 PM\nVenue : The Thesis Defense will be held on MICROSOFT TEAMS\nPlease click on the following link to join the Thesis Defense:\nMS Teams link\n\n\n\nABSTRACT\n\nEstimating Worst-Case E xecution Time (WCET) is paramount for developing Real-Time and Embedded sy stems. The operating system’s scheduler uses the estimated WCET to sched ule each task of these systems before the assigned deadline\, and failure may lead to catastrophic events such as resource damage or even life loss. These systems must satisfy the timing constraints. For instance\, it is e ssential to know that car airbags open fast enough to save lives. The majo r components required to estimate WCET are architecture or platform\, appl ication\, and worst-case data. In this regard\, we propose novel methods f or these components using machine learning techniques to estimate WCET saf ely and precisely to make these systems more predictable and reliable than traditional approaches.\n\n• Estimation of WCET on GPU architecture: Wi th the advances in machine learning and artificial intelligence in every f ield of life\, due to its tendency to solve many problems with accuracy\, it requires Graphics Processing Units (GPUs) to provide massive parallelis m for computation. GPUs are designed to provide high-performance through-p ut\, but their integration into real-time systems focuses on predictabilit y because most safety-critical applications have strict deadlines that nee d to be followed to avoid unwanted situations. We propose a Machine Learni ng approach to estimate the WCET of the GPU kernel from the binary of the applications. The approach helps reduce the significant design space explo ration in a short time. We use a measurement-based approach to train the m achine-learning model using different kernel instructions\, which can pred ict the WCET of the GPU kernel to detect timing misconfiguration in the la ter development phase of the systems.\n\n• Estimation of WCET on Mixed-C riticality Systems: In Mixed-Criticality (MC) Systems\, there is a trend o f having multiple functionalities upon a single shared computing platform for better cost and power efficiency. In this regard\, estimating the suit able optimistic WCET based on the different system modes is essential to p rovide these functionalities. A single application has assigned multiple W CETs based on the criticality of the system\, such as safety-critical\, mi ssion-critical\, and non-critical. We propose ESOMICS\, a novel method to estimate suitable optimistic WCET using a Machine Learning model. Our appr oach is based on the application’s source\, and the model is trained bas ed on the large data set. To prove the effectiveness of our approach\, we evaluated it with a newly defined metric EDT using an analytical solution that allows us to compute the optimum value in a mixed-criticality system based on experimentation. Our experimental results outperform all the prev ious state-of-the-art approaches.\n\n• Estimation of Worst-Case Data for WCET: Worst-Case Data which gives maximum execution time\, plays a vital role in the estimation of WCET. An evolutionary algorithm\, such as the Ge netic Algorithm\, has been employed to generate the Worst-Case Data. The c omplexity of an evolutionary algorithm requires the use of several computa tional resources. We propose a method to replace the hardware and simulato r used in the evolution process with Machine Learning models. This method reduces the overall time required to generate Worst-Case Data. Different m achine learning models are trained to integrate with genetic algorithms. T he feasibility of the proposed approach is validated using benchmarks from different domains. The results show the speedup in the generation of Wors t-Case Data.\n\n• Estimation of Early WCET: WCET is available to us in t he last stage of systems development when the hardware is available\, and the application code is compiled. Different methodologies measure the WCET \, but none give early insights into WCET\, whichis crucial for system dev elopment. If the system designers overestimate WCET in the early stage\, t hen it would lead to an overqualified system\, which will increase the cos t of the final product\, and if they underestimate WCET in the early stage \, then it would lead to financial loss as the system would not perform as expected. We propose to estimate early WCET using Machine Learning and De ep Neural Networks as an approximate predictor model for hardware architec ture and compiler. This model predicts the WCET based on the source code w ithout compiling and running on the hardware architecture. The resulting W CET needs to be revised to be used as an\nupper bound on the WCET. However \, getting these results in the early stages of system development is an e ssential prerequisite for the system’s dimension’s and configuration o f the hardware setup.\n\n\n\nALL ARE WELCOME CATEGORIES:Events,Thesis Defense END:VEVENT BEGIN:VTIMEZONE TZID:Asia/Kolkata X-LIC-LOCATION:Asia/Kolkata BEGIN:STANDARD DTSTART:20230706T145000 TZOFFSETFROM:+0530 TZOFFSETTO:+0530 TZNAME:IST END:STANDARD END:VTIMEZONE END:VCALENDAR