Accelerating Communication in On-Chip Interconnection Networks

Due to the ever-shrinking feature size in CMOS process technology, it is expected that future chip multiprocessors (CMPs) will have hundreds or thousands of processing cores. To support a massively large number of cores, packet-switched on-chip interconnection networks have become a de facto communication paradigm in CMPs. However, the on-chip networks have several drawbacks, such as limited on-chip resources, increasing communication latency, and insufficient communication bandwidth. In this dissertation, several schemes are proposed to accelerate communication in on-chip interconnection networks within area and cost budgets to overcome the problems. First, an early transition scheme for fully adaptive routing algorithms is proposed to improve network throughput. Within a limited number of resources, previously proposed fully adaptive routing algorithms have low utilization in escape channels. To increase utilization of escape channels, it transfers packets earlier before the normal channels are full. Second, a pseudo-circuit scheme is proposed to reduce network latency using communication temporal locality. Reducing per-hop router delay becomes more important for communication latency reduction in larger on-chip interconnection networks. To improve communication latency, the previous arbitration information is reused to bypass switch arbitration. For further acceleration, we also propose two aggressive schemes, pseudo-circuit speculation and buffer bypassing. Third, two handshake schemes are proposed to improve network throughput for nanophotonic interconnects. Nanophotonic interconnects have been proposed to replace metal wires with optical links in on-chip interconnection networks for low latency and power consumptions as well as high bandwidth. To minimize the average token waiting time of the nanophotonic interconnects, the traditional credit-based flow control is removed. Thus, the handshake schemes increase link utilization and enhance network throughput

Adaptation in pregnant women: a descriptive phenomenological study using Giorgi’s approach

Purpose This descriptive phenomenological study aimed to explore the lived experience and meaning of pregnant women’s adaptation. Methods Ten pregnant women from an ongoing Pregnant Couples' Cohort Study agreed to participate in this study. The data were collected through telephone in-depth interviews regarding what they experienced and felt about pregnancy adaptation. The qualitative data were analyzed using Giorgi’s method of descriptive phenomenology. Results Five core situation components were extracted from the raw data, along with 12 themes and 33 focal meanings. The five core situations were 1) first recognizing the pregnancy, 2) pregnancy-related changes, 3) the upcoming birth, 4) the postpartum period, and 5) parenting. The 12 themes were as follows: “anxiety, pressure, and embarrassment due to pregnancy,” “efforts to adapt to physical changes,” “efforts to adapt to the psychological difficulties of pregnancy,” “efforts to adapt to the financial burden and role changes caused by pregnancy,” “connecting with the fetus,” “adapting to a new marital relationship centering on the baby,” “the frustration of childbirth,” “fear of childbirth,” “postpartum care, need help with lactation planning,” “parenting beyond what I imagined” “dad’s willingness to participate in parenting,” and “career disconnect and consideration of workplace needs.” Conclusion We identified that pregnant women experience adaptation in physical, psychological, relational, and social aspects. The thematic clusters identified be used to develop nursing interventions to promote women's adaptation to pregnancy

Association of postpartum depression with postpartum posttraumatic stress disorder in Korean mothers: a longitudinal survey

Purpose This study aimed to determine the level of postpartum posttraumatic stress disorder (PTSD) and postpartum depression (PPD) in Korean mothers with healthy babies and to explore the factors related to postpartum PTSD. Methods This study used a longitudinal survey design to explore the levels and association of PPD and PTSD. Two hundred women were recruited during pregnancy and the data were collected via online survey from 166 mothers (84% retained) who gave birth to healthy babies, at two postpartum periods: Fear of childbirth was assessed at the 1st week; and spousal support, PPD, and postpartum PTSD were surveyed at the 4th week postpartum. Descriptive statistics, t-test, one-way analysis of variance, Chi square test, and multiple regression were done. Results The mean age of mothers was 33.12 (±3.97) years old. Postpartum PTSD was low (8.95±6.49) with 1.8% (n=3) at risk (≥19). PPD was also low (6.68±5.28) and 30.1% (n=50) were identified at risk (≥10). The comorbid rate of PPD with PTSD was 6%. Mothers who did not have a planned pregnancy had higher scores of PPD (t=–2.78, p=.008), whereas spousal support and PPD had negative relationship (r=–.21, p=.006). The overall explanatory power for postpartum PTSD was 55.2%, of which PPD was the only significant variable (β=.76, t=13.76, p<.001). Conclusion While only 1.8% was at risk of postpartum PTSD at 4 weeks postpartum, PPD prevalence was 30.1% and PPD was the only influential factor of postpartum PTSD. Assessment and counseling of PPD are required as well as screening for postpartum PTSD. More research is also needed on postpartum PTSD in Korean women

Development and application of a couple-centered antenatal education program in Korea

Purpose This study was conducted to develop a couple-centered antenatal education program and to test the program’s feasibility. Methods With a preliminary-experimental study design, 33 pregnant couples who were expecting their first child participated in this study. The program consisted of four sessions (1 hour/session/week) of education and counseling. Data were collected before and after the intervention from September 2018 to April 2019 at a women’s hospital in Daejeon, Korea, with demographic data forms, the Edinburgh Postnatal Depression Scale, Perceived Stress Scale, Maternal–Fetal Attachment Scale, Korean Newborn Care Confidence Scale, Wijma Delivery Expectancy/Experience Questionnaire, and Dyadic Adjustment Scale-10. Results The pregnant women and their husbands were on average 32.30±3.10 and 33.21±6.25 years old, respectively. The mean marriage duration was 2.34±1.63 years, the gestational age was 31.30±2.66 weeks, and 78.8% of the couples had a planned pregnancy. After the program, both the pregnant women and their husbands showed significant improvements in attachment to the fetus and confidence in providing infant care. Prenatal depression, prenatal stress, and fear of childbirth in pregnant women significantly decreased after completing the program. However, the dyadic adjustment score did not change significantly either in the pregnant women or their husbands. Conclusion A couple-centered antenatal education program seems to be effective for couples adjusting to parenthood, but further studies should explore ways to have a positive impact on couples’ relationships

Factors influencing prenatal and postpartum depression in Korea: a prospective cohort study

Purpose This study explored the prevalence of prenatal and postpartum depression in Korea and its influencing factors from 20 weeks of pregnancy to 12 weeks postpartum. Methods Using a prospective cohort study design, data on women’s depression and its influencing factors were collected at 20, 28, and 36 weeks of pregnancy and at 2, 6, and 12 weeks postpartum. The participants were 219 women and 181 spouses during pregnancy; and 183 mothers and 130 spouses after childbirth. Depressive symptoms were assessed by the Edinburgh Postnatal Depression Scale and influencing factors were measured by the Postpartum Depression Predictors Inventory-Revised, parity, and spousal depression. Results The prevalence of maternal depression was 10.5% to 21.5% before birth, and it was 22.4% to 32.8% postpartum. The prevalence slightly decreased during the prenatal period but peaked at 2 weeks postpartum. Antenatal depression was influenced by low socioeconomic status, lower self-esteem, having experienced prenatal depression, having experienced prenatal anxiety, a previous history of depression, lower social support, lower marital satisfaction, and higher life stress. The factors influencing postpartum depression were lower self-esteem, having experienced prenatal depression, having experienced prenatal anxiety, lower social support, lower marital satisfaction, and higher life stress, as well as infant temperament and maternal blues. Parity and spousal depression had no impacts. Conclusion The prevalence and influencing factors of maternal depression changed over time. Nurses need to screen women accordingly during the perinatal period and should provide education or counseling to prevent depression and promote adjustment to parenthood

Pseudo-Circuit: Accelerating Communication for On-Chip Interconnection Networks

As the number of cores on a single chip increases with more recent technologies, a packetswitched on-chip interconnection network has become a de facto communication paradigm for chip multiprocessors (CMPs). However, it is inevitable to suffer from high communication latency due to the increasing number of hops. In this paper, we attempt to accelerate network communication by exploiting communication temporal locality with minimal additional hardware cost in the existing state-of-the-art router architecture. We observe that packets frequently traverse through the same path chosen by previous packets due to repeated communication patterns, such as frequent pair-wise communication. Motivated by our observation, we propose a pseudo-circuit scheme. With previous communication patterns, the scheme reserves crossbar connections creating pseudo-circuits, sharable partial circuits within a single router. It reuses the previous arbitration information to bypass switch arbitration stage if the next flit traverses through the same pseudo-circuit. To accelerate communication performance further, we also propose two aggressive schemes; pseudo-circuit speculation and buffer bypassing. Pseudocircuit speculation creates more pseudo-circuits using unallocated crossbar connections while buffer 1 bypassing skips buffer writes to eliminate one pipeline stage. Our evaluation results using a cycle-accurate network simulator with traces from SPEComp2001, SPECjbb200, and Splash-2 show 16 % improvement in overall network performance and up to 5% reduction in average energy consumption in routers, compared to the state-of-the-art router architecture. Evaluated with synthetic workload traffic, this scheme shows performance improvement by up to 11%. Unlike previous techniques in the low-latency router design, our scheme can be applicable to any topology. When applied to the recently proposed topologies, such as Multidrop Express Cube [9] or Flattened Butterfly [13], it improves performance by up to 20%, resulting in more than 50 % latency reduction compared to a mesh topology. 1

An Improved Method for Prediction of Cancer Prognosis by Network Learning

Accurate identification of prognostic biomarkers is an important yet challenging goal in bioinformatics. Many bioinformatics approaches have been proposed for this purpose, but there is still room for improvement. In this paper, we propose a novel machine learning-based method for more accurate identification of prognostic biomarker genes and use them for prediction of cancer prognosis. The proposed method specifies the candidate prognostic gene module by graph learning using the generative adversarial networks (GANs) model, and scores genes using a PageRank algorithm. We applied the proposed method to multiple-omics data that included copy number, gene expression, DNA methylation, and somatic mutation data for five cancer types. The proposed method showed better prediction accuracy than did existing methods. We identified many prognostic genes and their roles in their biological pathways. We also showed that the genes identified from different omics data were complementary, which led to improved accuracy in prediction using multi-omics data