Leveraging Automatic High-Level Synthesis Resource Sharing to Maximize Dynamical Voltage Overscaling with Error Control

Approximate Computing has emerged as an alternative way to further reduce the power consumption of integrated circuits (ICs) by trading off errors at the output with simpler, more efficient logic. So far the main approaches in approximate computing have been to simplify the hardware circuit by pruning the circuit until the maximum error threshold is met. One of the critical issues, though, is the training data used to prune the circuit. The output error can significantly exceed the maximum error if the final workload does not match the training data. Thus, most previous work typically assumes that training data matches with the workload data distribution. In this work, we present a method that dynamically overscales the supply voltage based on different workload distribution at runtime. This allows to adaptively select the supply voltage that leads to the largest power savings while ensuring that the error will never exceed the maximum error threshold. This approach also allows restoring of the original error-free circuit if no matching workload distribution is found. The proposed method also leverages the ability of High-Level Synthesis (HLS) to automatically generate circuits with different properties by setting different synthesis constraints to maximize the available timing slack and, hence, maximize the power savings. Experimental results show that our proposed method works very well, saving on average 47.08% of power as compared to the exact output circuit and 20.25% more than a traditional approximation method.

Pandemic Metric with Confidence (PMC) Model to Predict Trustworthy Probability of Utilized COVID-19 Pandemic Trajectory across the Global

Lots of works aim to reveal the driving factors of COVID-19 pandemic trajectory yet ignore the confidence of utilized trajectory data, making consequent results suspicious. Hereby, we proposed a pandemic metric with confidence (PMC) model in the hypothesis of Bernoulli Distribution of nine trajectories reported from 113 countries. Results exhibit the average confidence of trajectories across the global not in excess of 12.1% with the error threshold configuration of 1E-5. In contrast, the 95% high confidence setting also failed to predict the trajectory containing the acceptable error not beyond 1E-3. Thus, a proposed trade-off strategy between two contradictory expections (>50% confidence, <1E-3 error) supports 61% of investigated countries to predict the varying trajectory with confidence beyond 50%. Moreover, PMC model recommend the remanent 39% countries to extend the proportion of populaces in COVID-19 detecting-pool to a suggested-value (>1% of populations), ensuing the average confidence up to 70%.

Improving Manufacturing Applications of Machine Learning by Understanding Defect Classification and the Critical Error Threshold

Machine learning (ML) is unlocking patterns and insight into data to provide financial value and knowledge for organizations. Use of machine learning in manufacturing environments is increasing, yet sometimes these applications fail to produce meaningful results. A critical review of how defects are classified is needed to appropriately apply machine learning in a production foundry and other manufacturing processes. Four elements associated with defect classification are proposed: Binary Acceptance Specifications, Stochastic Formation of Defects, Secondary Process Variation, and Visual Defect Inspection. These four elements create data space overlap, which influences the bias associated with training supervised machine learning algorithms. If this influence is significant enough, the predicted error of the model exceeds a critical error threshold (CET). There is no financial motivation to implement the ML model in the manufacturing environment if its error is greater than the CET. The goal is to bring awareness to these four elements, define the critical error threshold, and offer guidance and future study recommendations on data collection and machine learning that will increase the success of ML within manufacturing.

Validating an Adjustment to the Intermittent Critical Power Model for Elite Cyclists—Modeling W′ Balance During World Cup Team Pursuit Performances

Purpose: Modeling intermittent work capacity is an exciting development to the critical power model with many possible applications across elite sport. With the Skiba 2 model validated using subelite participants, an adjustment to the model’s recovery rate has been proposed for use in elite cyclists (Bartram adjustment). The team pursuit provides an intermittent supramaximal event with which to validate the modeling of W′ in this population. Methods: Team pursuit data of 6 elite cyclists competing for Australia at a Track World Cup were solved for end W′ values using both the Skiba 2 model and the Bartram adjustment. Each model’s success was evaluated by its ability to approximate end W′ values of 0 kJ, as well as a count of races modeled to within a predetermined error threshold of ±1.840 kJ. Results: On average, using the Skiba 2 model found end W′ values different from zero (P = .007; mean ± 95% confidence limit, –2.7 ± 2.0 kJ), with 3 out of 8 cases ending within the predetermined error threshold. Using the Bartram adjustment on average resulted in end W′ values that were not different from zero (P = .626; mean ± 95% confidence limit, 0.5 ± 2.5 kJ), with 4 out of 8 cases falling within the predetermined error threshold. Conclusions: On average, the Bartram adjustment was an improvement to modeling intermittent work capacity in elite cyclists, with the Skiba 2 model underestimating the rate of W′ recovery. In the specific context of modeling team pursuit races, all models were too variable for effective use; hence, individual recovery rates should be explored beyond population-specific rates.

A modified Delphi to define drug dosing errors in pediatric critical care

Background There is no globally accepted definition for dosing error in adult or pediatric practice. The definition of pediatric dosing error varies greatly in the literature. The objective of this study was to develop a framework, informed by a set of principles, for a clinician-based definition of drug dosing errors in critically ill children, and to identify the range that practitioners agree is a dosing error for different drug classes and clinical scenarios. Methods We conducted a nationwide three staged modified Delphi from May to December 2019. Expert clinicians included Canadian pediatric intensive care unit (PICU) physicians, pharmacists and nurses, with a least 5 years’ experience. Outcomes were underlying principles of drug dosing, and error thresholds, as defined by proportion above and below reference range, for common PICU medications and clinical scenarios. Results Forty-four participants met eligibility, and response rates were 95, 86 and 84% for all three rounds respectively. Consensus was achieved for 13 of 15 principles, and 23 of 30 error thresholds. An over-dosed drug that is intercepted, an under-dose of a possibly life-saving medication, dosing 50% above or below target range and not adjusting for a drug interaction were agreed principles of dosing error. Altough there remained much uncertainty in defining dosing error, expert clinicians agreed that, for most medication categories and clinical scenarios, dosing over or below 10% of reference range was considered an error threshold. Conclusion Dosing principles and threshold are complex in pediatric critical care, and expert clinicians were uncertain about whether many scenarios were considered in error. For most intermittent medications, dosing over 10% below or above reference range was considered a dosing error, although this was largely influenced by clinical context and drug properties. This consensus driven error threshold will help guide routine clinical dosing practice, standardized reporting and drug quality improvement in pediatric critical care.

A Modified Delphi to Define Drug Dosing Errors in Pediatric Critical Care

Background There is no globally accepted definition for dosing error in adult or pediatric practice. The definition of pediatric dosing error varies greatly in the literature. The objective of this study was to develop a framework, informed by a set of principles, for a clinician-based definition of drug dosing errors in critically ill children, and to identify the range that practitioners agree is a dosing error for different drug classes and clinical scenarios.Methods We conducted a nationwide three staged modified Delphi from May to December 2019. Expert clinicians included Canadian pediatric intensive care unit (PICU) physicians, pharmacists and nurses, with a least 5 years’ experience. Outcomes were underlying principles of drug dosing, and error thresholds, as defined by proportion above and below reference range, for common PICU medications and clinical scenarios. Results Forty-four participants met eligibility, and response rates were 95, 86 and 84% for all three rounds respectively. Consensus was achieved for 13 of 15 principles, and 23 of 30 error thresholds. An over-dosed drug that is intercepted, an under-dose of a possibly life-saving medication, dosing 50% above or below target range and not adjusting for a drug interaction were agreed principles of dosing error. Altough there remained much uncertainty in defining dosing error, expert clinicians agreed that, for most medication categories and clinical scenarios, dosing over or below 10% of reference range was considered an error threshold.Conclusion Dosing principles and threshold are complex in pediatric critical care, and expert clinicians were uncertain about whether many scenarios were considered in error. For most intermittent medications, dosing over 10% below or above reference range was considered a dosing error, although this was largely influenced by clinical context and drug properties. This consensus driven error threshold will help guide routine clinical dosing practice, standardized reporting and drug quality improvement in pediatric critical care.