AXI4MLIR: User-Driven automatic host code generation for custom AXI-Based accelerators

This document is a PrePrint . You can find it also in arXiv.org, with DOI: https://doi.org/10.48550/arXiv.2312.14821This paper addresses the need for automatic and efficient generation of host driver code for arbitrary custom AXI-based accelerators targeting linear algebra algorithms, an important workload in various applications, including machine learning and scientific computing. While existing tools have focused on automating accelerator prototyping, little attention has been paid to the host-accelerator interaction. This paper introduces AXI4MLIR, an extension of the MLIR compiler framework designed to facilitate the automated generation of host-accelerator driver code. With new MLIR attributes and transformations, AXI4MLIR empowers users to specify accelerator features (including their instructions) and communication patterns and exploit the host memory hierarchy. We demonstrate AXI4MLIR's versatility across different types of accelerators and problems, showcasing significant CPU cache reference reductions (up to 56%) and up to a 1.65x speedup compared to manually optimized driver code implementations. AXI4MLIR implementation is open-source and available at: t: https://github.com/AXI4MLIR/axi4mli

SECDA-TFLite: a toolkit for efficient development of FPGA-based DNN accelerators for edge inference

In this paper we propose SECDA-TFLite, a new open source toolkit for developing DNN hardware accelerators, integrated within the TFLite framework. The toolkit leverages the principles of SECDA, a hardware/software co-design methodology, to reduce the design time of optimized DNN inference accelerators on edge devices with FPGAs. With SECDA-TFLite, we reduce the initial setup costs associated with integrating a new accelerator design within a target DNN framework, allowing developers to focus on the design. SECDA-TFLite also includes modules for cost-effective SystemC simulation, profiling, and AXI-based data communication. As a case study, we use SECDA-TFLite to develop and evaluate three accelerator designs across seven common CNN models and two BERT-based models against an ARM A9 CPU-only baseline, achieving an average performance speedup across models of up to 3.4× for the CNN models and of up to 2.5× for the BERT-based models. Our code is available at https://github.com/gicLAB/SECDA-TFLite

The epigenetic clock is correlated with physical and cognitive fitness in the Lothian Birth Cohort 1936

Background: The DNA methylation-based 'epigenetic clock' correlates strongly with chronological age, but it is currently unclear what drives individual differences. We examine cross-sectional and longitudinal associations between the epigenetic clock and four mortality-linked markers of physical and mental fitness: lung function, walking speed, grip strength and cognitive ability. Methods: DNA methylation-based age acceleration (residuals of the epigenetic clock estimate regressed on chronological age) were estimated in the Lothian Birth Cohort 1936 at ages 70 (n=920), 73 (n=299) and 76 (n=273) years. General cognitive ability, walking speed, lung function and grip strength were measured concurrently. Cross-sectional correlations between age acceleration and the fitness variables were calculated. Longitudinal change in the epigenetic clock estimates and the fitness variables were assessed via linear mixed models and latent growth curves. Epigenetic age acceleration at age 70 was used as a predictor of longitudinal change in fitness. Epigenome-wide association studies (EWASs) were conducted on the four fitness measures. Results: Cross-sectional correlations were significant between greater age acceleration and poorer performance on the lung function, cognition and grip strength measures (r range: -0.07 to -0.05, P range: 9.7 x 10 to 0.024). All of the fitness variables declined over time but age acceleration did not correlate with subsequent change over 6 years. There were no EWAS hits for the fitness traits. Conclusions: Markers of physical and mental fitness are associated with the epigenetic clock (lower abilities associated with age acceleration). However, age acceleration does not associate with decline in these measures, at least over a relatively short follow-up

Investigating the relationship between DNA methylation age acceleration and risk factors for Alzheimer’s disease

This work was supported by a Alzheimer's Research UK Major Project grant (ARUK-PG2017B-10). Generation Scotland received core funding from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). We are grateful to all the families who took part, the general practitioners and the Scottish School of Primary Care for their help in recruiting them, and the whole Generation Scotland team that includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, health-care assistants and nurses. Genotyping of the GS:SFHS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland, and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” [STRADL];104036/Z/14/Z). DNA methylation data collection was funded by the Wellcome Trust Strategic Award (10436/Z/14/Z). The research was conducted in the University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE), part of the cross-council Lifelong Health and Wellbeing Initiative (MR/K026992/1); funding from the Biotechnology and Biological Sciences Research Council (BBSRC) and Medical Research Council (MRC) is gratefully acknowledged. CCACE supports I.J.D. with some additional support from the Dementias Platform UK (MR/L015382/1). A.M.M. and H.C.W. have received support from the Sackler Institute.Peer reviewedPublisher PD

Ethanol plant location and intensification vs. extensification of corn cropping in Kansas

This is the author final draft. Copyright 2014 Elsevier.Farmers' cropping decisions are a product of a complex mix of socio-economic, cultural, and natural environments in which factors operating at a number of different spatial scales affect how farmers ultimately decide to use their land in any given year or over a set of years. Some environmentalists are concerned that increased demand for corn driven by ethanol production is leading to conversion of non-cropland into corn production (which we label as “extensification”). Ethanol industry advocates counter that more than enough corn supply comes from crop switching to corn and increased yields (which we label as “intensification”). In this study, we determine whether either response to corn demand – intensification or extensification – is supported. This is determined through an analysis of land-use/land-cover (LULC) data that covers the state of Kansas and a measure of a corn demand shifter related to ethanol production – distance to the closest ethanol plant – between 2007 and 2009

Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP

Antidepressants demonstrate modest response rates in the treatment of major depressive disorder (MDD). Despite previous genome-wide association studies (GWAS) of antidepressant treatment response, the underlying genetic factors are unknown. Using prescription data in a population and family-based cohort (Generation Scotland: Scottish Family Health Study; GS:SFHS), we sought to define a measure of (a) antidepressant treatment resistance and (b) stages of antidepressant resistance by inferring antidepressant switching as non-response to treatment. GWAS were conducted separately for antidepressant treatment resistance in GS:SFHS and the Genome-based Therapeutic Drugs for Depression (GENDEP) study and then meta-analysed (meta-analysis n = 4213, cases = 358). For stages of antidepressant resistance, a GWAS on GS:SFHS only was performed (n = 3452). Additionally, we conducted gene-set enrichment, polygenic risk scoring (PRS) and genetic correlation analysis. We did not identify any significant loci, genes or gene sets associated with antidepressant treatment resistance or stages of resistance. Significant positive genetic correlations of antidepressant treatment resistance and stages of resistance with neuroticism, psychological distress, schizotypy and mood disorder traits were identified. These findings suggest that larger sample sizes are needed to identify the genetic architecture of antidepressant treatment response, and that population-based observational studies may provide a tractable approach to achieving the necessary statistical power

Diabetes foot complications and standardized mortality rate in type 2 diabetes

Aim: To quantify the impact of foot complications on mortality outcomes in people with type 2 diabetes (T2D), and how routinely measured factors might modulate that risk. Materials and Methods: Data for individuals with T2D for 2010-2020, from the Salford Integrated Care Record (Salford, UK), were extracted for laboratory and clinical data, and deaths. Annual expected deaths were taken from Office of National Statistics mortality data. An index of multiple deprivation (IMD) adjusted the standardized mortality ratio (SMR_IMD). Life years lost per death (LYLD) was estimated from the difference between expected and actual deaths. Results: A total of 11 806 T2D patients were included, with 5583 new diagnoses and 3921 deaths during 2010-2020. The number of expected deaths was 2135; after IMD adjustment, there were 2595 expected deaths. Therefore, excess deaths numbered 1326 (SMR_IMD 1.51). No foot complications were evident in n = 9857. This group had an SMR_IMD of 1.13 and 2.74 LYLD. In total, 2979 patients had any foot complication recorded. In this group, the SMD_IMR was 2.29; of these, 2555 (75%) had only one foot complication. Patients with a foot complication showed little difference in percentage HbA1c more than 58 mmol/mol. In multivariate analysis, for those with a foot complication and an albumin-to-creatinine ratio of more than 3 mg/mmol, the odds ratio (OR) for death was 1.93, and for an estimated glomerular filtration rate of less than 60 mL/min/1.73m2, the OR for death was 1.92. Conclusions: Patients with T2D but without a foot complication have an SMR_IMD that is only slightly higher than that of the general population. Those diagnosed with a foot complication have a mortality risk that is double that of those without T2D

Association of Body Mass Index with DNA Methylation and Gene Expression in Blood Cells and Relations to Cardiometabolic Disease: A Mendelian Randomization Approach

Background The link between DNA methylation, obesity, and adiposity-related diseases in the general population remains uncertain. Methods and Findings We conducted an association study of body mass index (BMI) and differential methylation for over 400,000 CpGs assayed by microarray in whole-blood-derived DNA from 3,743 participants in the Framingham Heart Study and the Lothian Birth Cohorts, with independent replication in three external cohorts of 4,055 participants. We examined variations in whole blood gene expression and conducted Mendelian randomization analyses to investigate the functional and clinical relevance of the findings. We identified novel and previously reported BMI-related differential methylation at 83 CpGs that replicated across cohorts; BMI-related differential methylation was associated with concurrent changes in the expression of genes in lipid metabolism pathways. Genetic instrumental variable analysis of alterations in methylation at one of the 83 replicated CpGs, cg11024682 (intronic to sterol regulatory element binding transcription factor 1 [SREBF1]), demonstrated links to BMI, adiposity-related traits, and coronary artery disease. Independent genetic instruments for expression of SREBF1 supported the findings linking methylation to adiposity and cardiometabolic disease. Methylation at a substantial proportion (16 of 83) of the identified loci was found to be secondary to differences in BMI. However, the cross-sectional nature of the data limits definitive causal determination. Conclusions We present robust associations of BMI with differential DNA methylation at numerous loci in blood cells. BMI-related DNA methylation and gene expression provide mechanistic insights into the relationship between DNA methylation, obesity, and adiposity-related diseases