Still a Fight to Get It Right: Verification in the Era of Machine Learning

Vasudevan, Shobha

doi:10.1109/icrc.2017.8123645

Cited by 6 publications

(2 citation statements)

References 59 publications

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“…Strictly speaking the concept of model verification does not apply, since there is not mathematical model to solve numerically; some authors use the term model verification to mean validation (e.g. [22]). Also, the concept of validation is quite different: to develop a phenomenological model we need a number of inputs sets for which the true (i.e.…”

Section: Phenomenological Modelsmentioning

confidence: 99%

Credibility of In Silico Trial Technologies—A Theoretical Framing

Viceconti

Juárez

Curreli

et al. 2020

IEEE J. Biomed. Health Inform.

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Different research communities have developed various approaches to assess the credibility of predictive models. Each approach usually works well for a specific type of model, and under some epistemic conditions that are normally satisfied within that specific research domain. Some regulatory agencies recently started to consider evidences of safety and efficacy on new medical products obtained using computer modelling and simulation (which is referred to as In Silico Trials); this has raised the attention in the computational medicine research community on the regulatory science aspects of this emerging discipline. But this poses a foundational problem: in the domain of biomedical research the use of computer modelling is relatively recent, without a widely accepted epistemic framing for model credibility. Also, because of the inherent complexity of living organisms, biomedical modellers tend to use a variety of modelling methods, sometimes mixing them in the solution of a single problem. In such context merely adopting credibility approaches developed within other research communities might not be appropriate. In this paper we propose a theoretical framing for assessing the credibility of a predictive models for In Silico Trials, which accounts for the epistemic specificity of this research field and is general enough to be used for different type of models.

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Section: Phenomenological Modelsmentioning

confidence: 99%

Credibility of In Silico Trial Technologies—A Theoretical Framing

Viceconti

Juárez

Curreli

et al. 2020

IEEE J. Biomed. Health Inform.

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“…Ideally, all possible inputs and internal design states should be exercised. Exhaustively testing all design state space is impractical [1] and the approach of writing independent test vectors to verify each state becomes highly infeasible given that about 70% of overall design activity is consumed by verification activities [1]. The practical approach used today to solve this problem is constrained random verification CRV.…”

Section: Introductionmentioning

confidence: 99%

Survey on Machine Learning Algorithms Enhancing the Functional Verification Process

Ismail¹,

Ghany

2021

Electronics

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The continuing increase in functional requirements of modern hardware designs means the traditional functional verification process becomes inefficient in meeting the time-to-market goal with sufficient level of confidence in the design. Therefore, the need for enhancing the process is evident. Machine learning (ML) models proved to be valuable for automating major parts of the process, which have typically occupied the bandwidth of engineers; diverting them from adding new coverage metrics to make the designs more robust. Current research of deploying different (ML) models prove to be promising in areas such as stimulus constraining, test generation, coverage collection and bug detection and localization. An example of deploying artificial neural network (ANN) in test generation shows 24.5× speed up in functionally verifying a dual-core RISC processor specification. Another study demonstrates how k-means clustering can reduce redundancy of simulation trace dump of an AHB-to-WHISHBONE bridge by 21%, thus reducing the debugging effort by not having to inspect unnecessary waveforms. The surveyed work demonstrates a comprehensive overview of current (ML) models enhancing the functional verification process from which an insight of promising future research areas is inferred.

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