28 research outputs found
Scalable functional validation of next generation SoCs
No full textSystem-on-Chips (SoCs) constitutes the primary backbone of modern embedded computing devices including many safety-critical applications e.g., autonomous vehicles, health care systems. The presence of any undetected bugs in these systems would have aberrant cost both in terms of safety and reliability and can cause loss of property or life. Hence, SoC validation is a crucial task to ensure the functional correctness of an SoC. The sheer size, presence of hundreds of concurrently executing heterogeneous IPs, vertical integration of SoC components e.g., hardware/firmware/software to realize multiple functionality, and application-level relevance of components present a new spectrum of validation challenges that have rendered the traditional microprocessor validation paradigm moot in the context of SoC validation. The challenges include observability enhancement and debug and diagnosis under the constraint of vertical integrations, identifying high-quality verification artifacts among others. In industrial practice, SoC validation is a manual, unsystematic, and ad hoc process that heavily relies on the expertise and the creativity of the validator. Consequently, there is an urgent need to develop scalable and efficient algorithms of industrial relevance to address this massive ongoing challenge of SoC validation.
This dissertation makes contributions to both post-silicon and pre-silicon validation of SoCs, with highly impactful contributions to next-generation post-silicon SoC validation. We use top-down analysis, a higher level of abstraction, and application relevance as the key ideas to automate post-silicon observability enhancement for industrial scale SoCs and scale observability to design that is more than 300x the size of designs that have been presented in the academic literature so far. Our observability enhancement solution can be applied at the netlist-level, behavioral level, and at the system-wide application level to select high-quality signals that are most beneficial for post-silicon debug and diagnosis. We apply a feature engineering based machine learning technique on the observed signal data to develop an automatic, scalable, and efficient post-silicon debug and diagnosis solution. The key idea is to learn the correct and erroneous design behavior automatically from trace data without prior design knowledge. We believe our debugging solution can automate post-silicon debug and diagnosis, where manual debugging is the norm. The quality of SoC verification and validation heavily depends on the quality of verification artifacts e.g., assertions. To automate and expedite identification of high-functional coverage assertions that are useful for regression analysis, localization, etc., we have also developed a comprehensive ranking scheme for assertions. The key idea is to identify assertions that capture important design behaviors by analyzing the design source code.
Our SoC validation solutions are scalable and efficient. We consistently show orders of magnitude speedup improvements over the state-of-the-art while objectively improving quality of results. We have shown that going forward application-level analysis is the key to scale post-silicon validation to industrial scale SoCs. Our proposed validation solutions can plug into the existing industrial validation process to introduce automation in the current unsystematic, ad hoc, manual settings with multiple order of magnitudes of benefit.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-08-01The student, Debjit Pal, accepted the attached license on 2019-07-11 at 09:37.The student, Debjit Pal, submitted this Dissertation for approval on 2019-07-11 at 09:53.This Dissertation was approved for publication on 2019-07-11 at 10:56.DSpace SAF Submission Ingestion Package generated from Vireo submission #14264 on 2019-11-26 at 13:05:22Made available in DSpace on 2019-11-26T20:49:27Z (GMT). No. of bitstreams: 3
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Previous issue date: 2019-07-11Embargo set by: Seth Robbins for item 112957
Lift date: 2021-11-26T20:49:41Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Only Restriction Lifted for Item 112957 on 2021-11-27T10:15:23Z
Collected Papers (on Physics, Artificial Intelligence, Health Issues, Decision Making, Economics, Statistics), Volume XI
No full textThis eleventh volume of Collected Papers includes 90 papers comprising 988 pages on Physics, Artificial Intelligence, Health Issues, Decision Making, Economics, Statistics, written between 2001-2022 by the author alone or in collaboration with the following 84 co-authors (alphabetically ordered) from 19 countries: Abhijit Saha, Abu Sufian, Jack Allen, Shahbaz Ali, Ali Safaa Sadiq, Aliya Fahmi, Atiqa Fakhar, Atiqa Firdous, Sukanto Bhattacharya, Robert N. Boyd, Victor Chang, Victor Christianto, V. Christy, Dao The Son, Debjit Dutta, Azeddine Elhassouny, Fazal Ghani, Fazli Amin, Anirudha Ghosha, Nasruddin Hassan, Hoang Viet Long, Jhulaneswar Baidya, Jin Kim, Jun Ye, Darjan Karabašević, Vasilios N. Katsikis, Ieva Meidutė-Kavaliauskienė, F. Kaymarm, Nour Eldeen M. Khalifa, Madad Khan, Qaisar Khan, M. Khoshnevisan, Kifayat Ullah,, Volodymyr Krasnoholovets, Mukesh Kumar, Le Hoang Son, Luong Thi Hong Lan, Tahir Mahmood, Mahmoud Ismail, Mohamed Abdel-Basset, Siti Nurul Fitriah Mohamad, Mohamed Loey, Mai Mohamed, K. Mohana, Kalyan Mondal, Muhammad Gulfam, Muhammad Khalid Mahmood, Muhammad Jamil, Muhammad Yaqub Khan, Muhammad Riaz, Nguyen Dinh Hoa, Cu Nguyen Giap, Nguyen Tho Thong, Peide Liu, Pham Huy Thong, Gabrijela Popović, Surapati Pramanik, Dmitri Rabounski, Roslan Hasni, Rumi Roy, Tapan Kumar Roy, Said Broumi, Saleem Abdullah, Muzafer Saračević, Ganeshsree Selvachandran, Shariful Alam, Shyamal Dalapati, Housila P. Singh, R. Singh, Rajesh Singh, Predrag S. Stanimirović, Kasan Susilo, Dragiša Stanujkić, Alexandra Şandru, Ovidiu Ilie Şandru, Zenonas Turskis, Yunita Umniyati, Alptekin Ulutaș, Maikel Yelandi Leyva Vázquez, Binyamin Yusoff, Edmundas Kazimieras Zavadskas, Zhao Loon Wang.
FERMENTABLE CARBOHYDRATES AND ENTERAL NUTRITION INTOLERANCE: A RETROSPECTIVE STUDY IN CRITICALLY ILL PATIENTS
No full textObjective. The aim of our study is to observe whether FODMAP content of EN is associated with either diarrhea, distention or gastric residual volumes in critically ill patients. Background. The role of enteral nutrition (EN) in the critically ill patient has been well established as it maintains gut integrity and is associated with improved outcomes and decreased morbidity. Intolerance to enteral nutrition (EN) is a common problem and is usually manifested as diarrhea, distention and elevated gastric residual volume. When this occurs, EN is often held or infused at a lower rate which causes calorie deficits, further compromising outcomes. Multiple factors including osmolality and ingredients of enteral formula need to be considered when assessing EN intolerance. Recently short chain carbohydrates known as FODMAPs (Fermentable Olgio Di- Mono- Saccharides And Polyols) has been shown to be associated with increased risk of intolerance. In colonic bacterial environment the FODMAPs produce rapidly fermentable substrate generating excessive amount of gas with characteristic symptoms including abdominal pain, discomfort, bloating, distention and altered bowel habits. Hamos et al demonstrated that hospitalized patients receiving Isosource 1.5 (an enteral formula with a lower FODMAP content) had a considerable reduction in risk of developing diarrhea. Methods. This retrospective observational study was approved by the Intstitutional Review Board of Mount Sinai Medical Center, Miami Beach Florida. The electronic health record, of all ICU admissions that were either on Peptamen AF ® or Replete ® from July 2012 through September 2013 were reviewed. Patients on EN for three or more consecutive days were included. Diarrhea was defined as one episode of loose or watery stool and/ or three or more stools within a twenty four hour period regardless of consistency. Distension was defined as a distended and/ or firm abdomen as reported in the nursing or dietitian record. Gastric residual was counted if the volume was greater than 250 cc on at least one occasion. Formulas were assigned to patients based on the preference of the physician or dietitian. Peptamen AF ® contains 5.2 g/ L of FODMAPs which consist of inulin (1.6 g/ L) and fructooligosaccharides (3.6 g/ L), while Replete ® does not contain any. Results. Among 221 patients the incidence of gastric residuals was higher in the Peptamen AF® group (18.9% vs 9.2%, P \u3c 0.05, odds ratio (OR) of 2.31). There was no significant difference in the occurrence of either diarrhea (P=0.847) or distension (P=0.087). We observed increased occurrence of diarrhea with longer duration of EN (P\u3c0.05, OR 1.05). Abdominal distension was associated with renal replacement therapy (RRT) (P\u3c0.05, OR 2.906), and gastric residuals was predicted by inotropes (P\u3c0.05, OR 4.047) and RRT (P=0.05, OR 3.014). Logistic regression failed to show any significant differences in diarrhea, distension, or gastric residuals between the two groups. Conclusion. Duration of enteral nutrition, RRT and inotropes were significant independent predictors of EN intolerance. Although FODMAPs have been associated with diarrhea, it does not influence EN intolerance in critically ill patients. This observation may be influenced by multiple variables increasing the risk for gastric intolerance. Further prospective studies are warranted in this unique population to evaluate the causes of intolerance. Grants. No grants to disclos
HIV Risk profile and its socio-demographic correlates among long-distance truckers in West Bengal, India: Evidence from national HIV sentinel surveillance 2017
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Selexipag in Pulmonary Arterial Hypertension: Most Updated Evidence From Recent Preclinical and Clinical Studies
No full textMatrix metalloproteinase3 gene promoter polymorphisms and their haplotypes are associated with gastric cancer risk in eastern Indian population
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