KAPow: A System Identification Approach to Online Per-Module Power Estimation in FPGA Designs

In a modern FPGA system-on-chip design, it is often insufficient to simply assess the total power consumption of the entire circuit by design-time estimation or runtime power rail measurement. Instead, to make better runtime decisions, it is desirable to understand the power consumed by each individual module in the system. In this work, we combine boardlevel power measurements with register-level activity counting to build an online model that produces a breakdown of power consumption within the design. Online model refinement avoids the need for a time-consuming characterisation stage and also allows the model to track long-term changes to operating conditions. Our flow is named KAPow, a (loose) acronym for ‘K’ounting Activity for Power estimation, which we show to be accurate, with per-module power estimates as close to ±5mW of true measurements, and to have low overheads. We also demonstrate an application example in which a permodule power breakdown can be used to determine an efficient mapping of tasks to modules and reduce system-wide power consumption by over 8%

Machine Learning Techniques to Evaluate the Approximation of Utilization Power in Circuits

The need for products that are more streamlined, more useful, and have longer battery lives is rising in today's culture. More components are being integrated onto smaller, more complex chips in order to do this. The outcome is higher total power consumption as a result of increased power dissipation brought on by dynamic and static currents in integrated circuits (ICs). For effective power planning and the precise application of power pads and strips by floor plan engineers, estimating power dissipation at an early stage is essential. With more information about the design attributes, power estimation accuracy increases. For a variety of applications, including function approximation, regularization, noisy interpolation, classification, and density estimation, they offer a coherent framework. RBFNN training is also quicker than training multi-layer perceptron networks. RBFNN learning typically comprises of a linear supervised phase for computing weights, followed by an unsupervised phase for determining the centers and widths of the Gaussian basis functions. This study investigates several learning techniques for estimating the synaptic weights, widths, and centers of RBFNNs. In this study, RBF networks—a traditional family of supervised learning algorithms—are examined.  Using centers found using k-means clustering and the square norm of the network coefficients, respectively, two popular regularization techniques are examined. It is demonstrated that each of these RBF techniques are capable of being rewritten as data-dependent kernels. Due to their adaptability and quicker training time when compared to multi-layer perceptron networks, RBFNNs present a compelling option to conventional neural network models. Along with experimental data, the research offers a theoretical analysis of these techniques, indicating competitive performance and a few advantages over traditional kernel techniques in terms of adaptability (ability to take into account unlabeled data) and computing complexity. The research also discusses current achievements in using soft k-means features for image identification and other tasks

Prognostics and health management of light emitting diodes

Prognostics is an engineering process of diagnosing, predicting the remaining useful life and estimating the reliability of systems and products. Prognostics and Health Management (PHM) has emerged in the last decade as one of the most efficient approaches in failure prevention, reliability estimation and remaining useful life predictions of various engineering systems and products. Light Emitting Diodes (LEDs) are optoelectronic micro-devices that are now replacing traditional incandescent and fluorescent lighting, as they have many advantages including higher reliability, greater energy efficiency, long life time and faster switching speed. Even though LEDs have high reliability and long life time, manufacturers and lighting systems designers still need to assess the reliability of LED lighting systems and the failures in the LED. This research provides both experimental and theoretical results that demonstrate the use of prognostics and health monitoring techniques for high power LEDs subjected to harsh operating conditions. Data driven, model driven and fusion prognostics approaches are developed to monitor and identify LED failures, based on the requirement for the light output power. The approaches adopted in this work are validated and can be used to assess the life of an LED lighting system after their deployment based on the power of the light output emitted. The data driven techniques are only based on monitoring selected operational and performance indicators using sensors whereas the model driven technique is based on sensor data as well as on a developed empirical model. Fusion approach is also developed using the data driven and the model driven approaches to the LED. Real-time implementation of developed approaches are also investigated and discussed

Critical review on improved electrochemical impedance spectroscopy-cuckoo search-elman neural network modeling methods for whole-life-cycle health state estimation of lithium-ion battery energy storage systems.

Efficient and accurate health state estimation is crucial for lithium-ion battery (LIB) performance monitoring and economic evaluation. Effectively estimating the health state of LIBs online is the key but is also the most difficult task for energy storage systems. With high adaptability and applicability advantages, battery health state estimation based on data-driven techniques has attracted extensive attention from researchers around the world. Artificial neural network (ANN)-based methods are often used for state estimations of LIBs. As one of the ANN methods, the Elman neural network (ENN) model has been improved to estimate the battery state more efficiently and accurately. In this paper, an improved ENN estimation method based on electrochemical impedance spectroscopy (EIS) and cuckoo search (CS) is established as the EIS-CS-ENN model to estimate the health state of LIBs. Also, the paper conducts a critical review of various ANN models against the EIS-CS-ENN model. This demonstrates that the EIS-CS-ENN model outperforms other models. The review also proves that, under the same conditions, selecting appropriate health indicators (HIs) according to the mathematical modeling ability and state requirements are the keys in estimating the health state efficiently. In the calculation process, several evaluation indicators are adopted to analyze and compare the modeling accuracy with other existing methods. Through the analysis of the evaluation results and the selection of HIs, conclusions and suggestions are put forward. Also, the robustness of the EIS-CS-ENN model for the health state estimation of LIBs is verified

The state of quantum computing applications in health and medicine

Quantum computing hardware and software have made enormous strides over the last years. Questions around quantum computing's impact on research and society have changed from "if" to "when/how". The 2020s have been described as the "quantum decade", and the first production solutions that drive scientific and business value are expected to become available over the next years. Medicine, including fields in healthcare and life sciences, has seen a flurry of quantum-related activities and experiments in the last few years (although medicine and quantum theory have arguably been entangled ever since Schr\"odinger's cat). The initial focus was on biochemical and computational biology problems; recently, however, clinical and medical quantum solutions have drawn increasing interest. The rapid emergence of quantum computing in health and medicine necessitates a mapping of the landscape. In this review, clinical and medical proof-of-concept quantum computing applications are outlined and put into perspective. These consist of over 40 experimental and theoretical studies from the last few years. The use case areas span genomics, clinical research and discovery, diagnostics, and treatments and interventions. Quantum machine learning (QML) in particular has rapidly evolved and shown to be competitive with classical benchmarks in recent medical research. Near-term QML algorithms, for instance, quantum support vector classifiers and quantum neural networks, have been trained with diverse clinical and real-world data sets. This includes studies in generating new molecular entities as drug candidates, diagnosing based on medical image classification, predicting patient persistence, forecasting treatment effectiveness, and tailoring radiotherapy. The use cases and algorithms are summarized and an outlook on medicine in the quantum era, including technical and ethical challenges, is provided