Reduction of Coupling Transition by Using Multiple Encoding Technique in Data Bus and Its Power Analysis

Shavali, V.; Reddy, G. M. Sreerama; Reddy, P. Ramana

doi:10.1007/978-981-13-3765-9_36

Cited by 1 publication

(1 citation statement)

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“…Statistical approaches were described for the estimation of peak loss. Additional work provided the register-transistor-logic (RTL) forecast of changing data using predictive methods suggested by Shavali et al [22]. Unlike previous approaches, the study focused on the network level forecast employed throughout the semiconductor design sign-off phase.…”

Section: Background To the Energy Prediction Modelsmentioning

confidence: 99%

Power Prediction of VLSI Circuits Using Machine Learning

Poovannan¹,

Karthik²

2023

Computers, Materials &Amp; Continua

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The difference between circuit design stage and time requirements has broadened with the increasing complexity of the circuit. A big database is needed to undertake important analytical work like statistical method, heat research, and IR-drop research that results in extended running times. This unit focuses on the assessment of test strength. Because of the enormous number of successful designs for current models and the unnecessary time required for every test, maximum energy ratings with all tests cannot be achieved. Nevertheless, test safety is important for producing trustworthy findings to avoid loss of output and harm to the chip. Generally, effective power assessment is only possible in a limited sample of pre-selected experiments. Thus, a key objective is to find the experiments that might give the worst situations again for testing power. It offers a machine-based circuit power estimation (ML-CPE) system for the selection of exams. Two distinct techniques of predicting are utilized. Firstly, to find testings with power dissipation, it forecasts the behavior of testing. Secondly, the change movement and energy data are linked to the semiconductor design, identifying small problem areas. Several types of algorithms are utilized. In particular, the methods compared. The findings show great accuracy and efficiency in forecasting. That enables such methods suitable for selecting the worst scenario.

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Section: Background To the Energy Prediction Modelsmentioning

confidence: 99%