IP-Enabled C/C++ Based High Level Synthesis: A Step towards Better Designer Productivity and Design Performance

Intellectual property (IP) core based design is an emerging design methodology to deal with increasing chip design complexity. C/C++ based high level synthesis (HLS) is also gaining traction as a design methodology to deal with increasing design complexity. In the work presented here, we present a design methodology that combines these two individual methodologies and is therefore more powerful. We discuss our proposed methodology in the context of supporting efficient hardware synthesis of a class of mathematical functions without altering original C/C++ source code. Additionally, we also discuss and propose methods to integrate legacy IP cores in existing HLS flows. Relying on concepts from the domains of program recognition and optimized low level implementations of such arithmetic functions, the described design methodology is a step towards intelligent synthesis where application characteristics are matched with specific architectural resources and relevant IP cores in a transparent manner for improved area-delay results. The combined methodology is more aware of the target hardware architecture than the conventional HLS flow. Implementation results of certain compute kernels from a commercial tool Vivado-HLS as well as proposed flow are also compared to show that proposed flow gives better results

YOLORe-IDNet: An Efficient Multi-Camera System for Person-Tracking

The growing need for video surveillance in public spaces has created a demand for systems that can track individuals across multiple cameras feeds in real-time. While existing tracking systems have achieved impressive performance using deep learning models, they often rely on pre-existing images of suspects or historical data. However, this is not always feasible in cases where suspicious individuals are identified in real-time and without prior knowledge. We propose a person-tracking system that combines correlation filters and Intersection Over Union (IOU) constraints for robust tracking, along with a deep learning model for cross-camera person re-identification (Re-ID) on top of YOLOv5. The proposed system quickly identifies and tracks suspect in real-time across multiple cameras and recovers well after full or partial occlusion, making it suitable for security and surveillance applications. It is computationally efficient and achieves a high F1-Score of 79% and an IOU of 59% comparable to existing state-of-the-art algorithms, as demonstrated in our evaluation on a publicly available OTB-100 dataset. The proposed system offers a robust and efficient solution for the real-time tracking of individuals across multiple camera feeds. Its ability to track targets without prior knowledge or historical data is a significant improvement over existing systems, making it well-suited for public safety and surveillance applications

Towards Efficient and Scalable Acceleration of Online Decision Tree Learning on FPGA

Decision trees are machine learning models commonly used in various application scenarios. In the era of big data, traditional decision tree induction algorithms are not suitable for learning large-scale datasets due to their stringent data storage requirement. Online decision tree learning algorithms have been devised to tackle this problem by concurrently training with incoming samples and providing inference results. However, even the most up-to-date online tree learning algorithms still suffer from either high memory usage or high computational intensity with dependency and long latency, making them challenging to implement in hardware. To overcome these difficulties, we introduce a new quantile-based algorithm to improve the induction of the Hoeffding tree, one of the state-of-the-art online learning models. The proposed algorithm is light-weight in terms of both memory and computational demand, while still maintaining high generalization ability. A series of optimization techniques dedicated to the proposed algorithm have been investigated from the hardware perspective, including coarse-grained and fine-grained parallelism, dynamic and memory-based resource sharing, pipelining with data forwarding. We further present a high-performance, hardware-efficient and scalable online decision tree learning system on a field-programmable gate array (FPGA) with system-level optimization techniques. Experimental results show that our proposed algorithm outperforms the state-of-the-art Hoeffding tree learning method, leading to 0.05% to 12.3% improvement in inference accuracy. Real implementation of the complete learning system on the FPGA demonstrates a 384x to 1581x speedup in execution time over the state-of-the-art design.Comment: appear as a conference paper in FCCM 201

HL-Pow: A Learning-Based Power Modeling Framework for High-Level Synthesis

High-level synthesis (HLS) enables designers to customize hardware designs efficiently. However, it is still challenging to foresee the correlation between power consumption and HLS-based applications at an early design stage. To overcome this problem, we introduce HL-Pow, a power modeling framework for FPGA HLS based on state-of-the-art machine learning techniques. HL-Pow incorporates an automated feature construction flow to efficiently identify and extract features that exert a major influence on power consumption, simply based upon HLS results, and a modeling flow that can build an accurate and generic power model applicable to a variety of designs with HLS. By using HL-Pow, the power evaluation process for FPGA designs can be significantly expedited because the power inference of HL-Pow is established on HLS instead of the time-consuming register-transfer level (RTL) implementation flow. Experimental results demonstrate that HL-Pow can achieve accurate power modeling that is only 4.67% (24.02 mW) away from onboard power measurement. To further facilitate power-oriented optimizations, we describe a novel design space exploration (DSE) algorithm built on top of HL-Pow to trade off between latency and power consumption. This algorithm can reach a close approximation of the real Pareto frontier while only requiring running HLS flow for 20% of design points in the entire design space.Comment: published as a conference paper in ASP-DAC 202

Asymptomatic Proteinuria and Hematuria in School Going Children

Background: This study was undertaken because many cases of asymptomatic proteinuria and hematuria are present in school children.Aims and Objectives:The study was under taken to evaluate asymptomatic proteinuria and hematuria in 100 school children of both sexes from 6 to 15 years of age. Material and Methods: Samples were collected randomly from students of different classes at the Government Kannada Primary School, K H B Colony,Bijapur, Karnataka (India). The midstream urine sample was collected in a clean wide mouth jar,examined by dipsticks for proteinuria and hematuria by microscopy. Results:We found that asymptomatic proteinuria and hematuria was more prevalent amongst the children between 10 to 13 years of age group with female predominance i.e.16% proteinuria and 5% of microscopic hematuria respectively. Conclusion:By this study we conclude that it would be possible to screen a large population of children for asymptomatic proteinuria and hematuria at a relatively low cost

Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017 : a systematic analysis for the Global Burden of Disease Study 2017

Background Global development goals increasingly rely on country-specific estimates for benchmarking a nation's progress. To meet this need, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 estimated global, regional, national, and, for selected locations, subnational cause-specific mortality beginning in the year 1980. Here we report an update to that study, making use of newly available data and improved methods. GBD 2017 provides a comprehensive assessment of cause-specific mortality for 282 causes in 195 countries and territories from 1980 to 2017. Methods The causes of death database is composed of vital registration (VR), verbal autopsy (VA), registry, survey, police, and surveillance data. GBD 2017 added ten VA studies, 127 country-years of VR data, 502 cancer-registry country-years, and an additional surveillance country-year. Expansions of the GBD cause of death hierarchy resulted in 18 additional causes estimated for GBD 2017. Newly available data led to subnational estimates for five additional countries Ethiopia, Iran, New Zealand, Norway, and Russia. Deaths assigned International Classification of Diseases (ICD) codes for non-specific, implausible, or intermediate causes of death were reassigned to underlying causes by redistribution algorithms that were incorporated into uncertainty estimation. We used statistical modelling tools developed for GBD, including the Cause of Death Ensemble model (CODErn), to generate cause fractions and cause specific death rates for each location, year, age, and sex. Instead of using UN estimates as in previous versions, GBD 2017 independently estimated population size and fertility rate for all locations. Years of life lost (YLLs) were then calculated as the sum of each death multiplied by the standard life expectancy at each age. All rates reported here are age-standardised. Findings At the broadest grouping of causes of death (Level 1), non-communicable diseases (NC Ds) comprised the greatest fraction of deaths, contributing to 73.4% (95% uncertainty interval [UI] 72.5-74.1) of total deaths in 2017, while communicable, maternal, neonatal, and nutritional (CMNN) causes accounted for 186% (17.9-19.6), and injuries 8.0% (7.7-8.2). Total numbers of deaths from NCD causes increased from 2007 to 2017 by 22.7% (21.5-23.9), representing an additional 7.61 million (7. 20-8.01) deaths estimated in 2017 versus 2007. The death rate from NCDs decreased globally by 7.9% (7.08.8). The number of deaths for CMNN causes decreased by 222% (20.0-24.0) and the death rate by 31.8% (30.1-33.3). Total deaths from injuries increased by 2.3% (0-5-4-0) between 2007 and 2017, and the death rate from injuries decreased by 13.7% (12.2-15.1) to 57.9 deaths (55.9-59.2) per 100 000 in 2017. Deaths from substance use disorders also increased, rising from 284 000 deaths (268 000-289 000) globally in 2007 to 352 000 (334 000-363 000) in 2017. Between 2007 and 2017, total deaths from conflict and terrorism increased by 118.0% (88.8-148.6). A greater reduction in total deaths and death rates was observed for some CMNN causes among children younger than 5 years than for older adults, such as a 36.4% (32.2-40.6) reduction in deaths from lower respiratory infections for children younger than 5 years compared with a 33.6% (31.2-36.1) increase in adults older than 70 years. Globally, the number of deaths was greater for men than for women at most ages in 2017, except at ages older than 85 years. Trends in global YLLs reflect an epidemiological transition, with decreases in total YLLs from enteric infections, respirator}, infections and tuberculosis, and maternal and neonatal disorders between 1990 and 2017; these were generally greater in magnitude at the lowest levels of the Socio-demographic Index (SDI). At the same time, there were large increases in YLLs from neoplasms and cardiovascular diseases. YLL rates decreased across the five leading Level 2 causes in all SDI quintiles. The leading causes of YLLs in 1990 neonatal disorders, lower respiratory infections, and diarrhoeal diseases were ranked second, fourth, and fifth, in 2017. Meanwhile, estimated YLLs increased for ischaemic heart disease (ranked first in 2017) and stroke (ranked third), even though YLL rates decreased. Population growth contributed to increased total deaths across the 20 leading Level 2 causes of mortality between 2007 and 2017. Decreases in the cause-specific mortality rate reduced the effect of population growth for all but three causes: substance use disorders, neurological disorders, and skin and subcutaneous diseases. Interpretation Improvements in global health have been unevenly distributed among populations. Deaths due to injuries, substance use disorders, armed conflict and terrorism, neoplasms, and cardiovascular disease are expanding threats to global health. For causes of death such as lower respiratory and enteric infections, more rapid progress occurred for children than for the oldest adults, and there is continuing disparity in mortality rates by sex across age groups. Reductions in the death rate of some common diseases are themselves slowing or have ceased, primarily for NCDs, and the death rate for selected causes has increased in the past decade. Copyright (C) 2018 The Author(s). Published by Elsevier Ltd.Peer reviewe