IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society

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Computer-aided analysis of electronic circuits: Algorithms and computational techniques

Prediction of Type 2 Diabetes using Machine Learning Classification Methods

Current technology provides an efficient way of monitoring the personal health of individuals. Bluetooth Low Energy (BLE)-based sensors can be considered as a solution for monitoring personal vital signs data. In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device, real-time data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. BLEs were used to gather users’ vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG) from sensor nodes to smartphones, while real-time data processing was utilized to manage the large amount of continuously generated sensor data. The proposed real-time data processing utilized Apache Kafka as a streaming platform and MongoDB to store the sensor data from the patient. The results show that commercial versions of the BLE-based sensors and the proposed real-time data processing are sufficiently efficient to monitor the vital signs data of diabetic patients. Furthermore, machine learning–based classification methods were tested on a diabetes dataset and showed that a Multilayer Perceptron can provide early prediction of diabetes given the user’s sensor data as input. The results also reveal that Long Short-Term Memory can accurately predict the future BG level based on the current sensor data. In addition, the proposed diabetes classification and BG prediction could be combined with personalized diet and physical activity suggestions in order to improve the health quality of patients and to avoid critical conditions in the future.

A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing.

Most screening tests for T2DM in use today were developed using multivariate regression methods that are often further simplified to allow transformation into a scoring formula. The increasing volume of electronically collected data opened the opportunity to develop more complex, accurate prediction models that can be continuously updated using machine learning approaches. This study compares machine learning-based prediction models (i.e. Glmnet, RF, XGBoost, LightGBM) to commonly used regression models for prediction of undiagnosed T2DM. The performance in prediction of fasting plasma glucose level was measured using 100 bootstrap iterations in different subsets of data simulating new incoming data in 6-month batches. With 6 months of data available, simple regression model performed with the lowest average RMSE of 0.838, followed by RF (0.842), LightGBM (0.846), Glmnet (0.859) and XGBoost (0.881). When more data were added, Glmnet improved with the highest rate (+ 3.4%). The highest level of variable selection stability over time was observed with LightGBM models. Our results show no clinically relevant improvement when more sophisticated prediction models were used. Since higher stability of selected variables over time contributes to simpler interpretation of the models, interpretability and model calibration should also be considered in development of clinical prediction models.

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Early detection of type 2 diabetes mellitus using machine learning-based prediction models.

Type 2 diabetes mellitus prediction model based on data mining

In this paper, we propose a novel simulation algorithm for large scale structured power grid networks. The new method formulates the traditional linear system as a special two-dimension Poisson equation and solves it using an analytical expressions based on FFT technique. The computation complexity of the new algorithm is O(NlgN), which is much smaller than the traditional solver's complexity O(N1.5) for sparse matrices, such as the SuperLU solver and the PCG solver. Also, due to the special formulation, graphic process unit (GPU) can be explored to further speed up the algorithm. Experimental results show that the new algorithm is stable and can achieve 100X speed up on GPU over the widely used SuperLU solver with very little memory footprint.

GPU friendly fast Poisson solver for structured power grid network analysis

Electromigration (EM) is considered to be one of the most important reliability issues for current and future ICs in 10nm technology and below. In this paper we focus on the EM stress evaluation for one-dimensional multi-segment interconnect wires in which all the segments have the same direction, which is a common routing structure for power grid networks. The proposed method, which is based on integral transform technique, could efficiently calculate the hydrostatic stress evolution for multi-segment metal wires stressed with different current densities. The new method can also naturally consider the pre-existing residual stresses coming from thermal or other stress sources. Based on this new transient EM assessment method, a full-chip assessment algorithm for power grid networks is then proposed. The new algorithm is also based on the IR-drop metrics for failure assessment of the power grid networks. However, it finds the precise location and time of EM-induced void nucleation by directly checking the time-changing hydrostatic stresses of all the wires. The resulting EM assessment method can ensure sufficient accuracy of the EM verification for large scale power grid networks without sacrificing the efficiency. The accuracy of the proposed transient analysis approach is validated against the numerical analysis. Also the resulting EM-aware full-chip power grid reliability analysis has been demonstrated and compared with existing methods.

Physics-based electromigration modeling and assessment for multi-segment interconnects in power grid networks

Electromigration (EM) becomes one of the most challenging reliability issues for current and future ICs in 10nm technology and below. In this paper, we propose a new analsys method for the EM hydrostatic stress evolution for multi-branch interconnect trees, which is the foundation of the EM reliability assessment for large scale on-chip interconnect networks, such as power grid networks. The proposed method, which is based on eigenfunctions technique, could efficiently calculate the hydrostatic stress evolution for multi-branch interconnect trees stressed with different current densities and non-uniformly distributed thermal effects. The new method can also accommodate the pre-existing residual stresses coming from thermal or other stress sources. The proposed method solves the partial differential equations of EM stress more efficiently since it does not require any discretization either spatially or temporall, which is in contrast to numerical methods such as finite difference method and finite element method. The accuracy of the proposed transient analysis approach is validated against the analytical solution and commercial tools. The efficiency of the proposed method is demonstrated and compared to finite difference method. The proposed method is 10X∼100X times faster than finite difference method and scales better for larger interconnect trees.

Fast physics-based electromigration analysis for multi-branch interconnect trees

Graph neural networks (GNN) have achieved state-of-the-art performance on various industrial tasks. However, the poor efficiency of GNN inference and frequent Out-of-Memory (OOM) problem limit the successful application of GNN on edge computing platforms. To tackle these problems, a feature decomposition approach is proposed for memory efficiency optimization of GNN inference. The proposed approach could achieve outstanding optimization on various GNN models, covering a wide range of datasets, which speeds up the inference by up to 3×. Furthermore, the proposed feature decomposition could significantly reduce the peak memory usage (up to 5× in memory efficiency improvement) and mitigate OOM problems during GNN inference.

Xiaoyi Wang

Papers

Type 2 diabetes mellitus prediction model based on data mining

GPU friendly fast Poisson solver for structured power grid network analysis

Physics-based electromigration modeling and assessment for multi-segment interconnects in power grid networks

Fast physics-based electromigration analysis for multi-branch interconnect trees

Brief Industry Paper: optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms