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Master Thesis: Early Prediction and Optimization of power, Perfomance and Area

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Key Responsibilities

Correlating characteristics visible at RTL level with PPA results obtained after synthesis or physical implementation Analysing the PPA impact of common design structures,

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## Join our Team About this opportunity Would you like to help digital design teams understand how an RTL design is likely to perform before completing time-consuming synthesis and physical implementation? Power, performance and area, commonly referred to as PPA, are critical design objectives in advanced digital systems. However, reliable PPA results are often available only at a relatively late stage of the development process. At that point, addressing an inefficient architectural or RTL design choice may require significant redesign and repeated implementation runs. Early estimates derived from RTL can differ considerably from final implementation results because they do not fully account for factors such as technology mapping, switching activity, buffering, placement and routing. In this thesis, you will investigate how RTL characteristics, simulation activity data and results from completed implementation runs can be combined to improve early PPA prediction. The goal is not only to produce more accurate estimates, but also to turn the findings into practical, actionable feedback for RTL and IP designers. What you will do You will work with one or more representative RTL designs and the associated simulation, synthesis, power and implementation data. You will establish PPA baselines and investigate how different RTL structures, architectural decisions and synthesis configurations affect power consumption, timing performance and silicon area. You will have the opportunity to help shape the detailed direction of the thesis. Areas of investigation may include: Correlating characteristics visible at RTL level with PPA results obtained after synthesis or physical implementation Analysing the PPA impact of common design structures, including: Multipliers and adders Filters and multiplexers Combinational and register-based logic Pipelines, register placement and clock-gating opportunities Comparing alternative RTL or architectural implementations that provide equivalent functionality Evaluating synthesis configurations and optimisation priorities for power, timing and area Using simulation activity data, such as VCD or FSDB data, to improve power analysis Exploring data-driven, automated or AI-assisted methods for identifying PPA optimisation opportunities Developing a reusable methodology or prototype flow that provides actionable feedback to RTL and IP designers Verifying that proposed optimisations preserve functional correctness and satisfy relevant timing and area requirements The expected outcome is a systematic and reusable approach for identifying PPA optimisation opportunities earlier in the development process. By moving this analysis closer to the RTL design stage, the work could improve PPA predictability, reduce manual investigation and limit the number of iterations between RTL development, synthesis and physical implementation. The skills you bring You are pursuing a Master’s degree in Electrical Engineering, Computer Engineering, Computer Science, Embedded Systems or a related field You have knowledge of digital design and computer architecture You have a basic understanding of RTL design using Verilog, SystemVerilog or VHDL You are interested in power, performance and area analysis You can work methodically with experimental data and evaluate alternative design solutions You are eager to learn about semiconductor design, synthesis and physical implementation flows Experience in one or more of the following areas would be an advantage: Logic synthesis or ASIC design flows Digital simulation and waveform activity data Scripting or programming using Python, Tcl or a similar language Data analysis, machine learning or AI-assisted engineering methods

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About this job

Posted
Today
Employer
has 104 open jobs on Jobera.

Listing Details

Posted
November 3, 2026
First seen
November 3, 2026
Last seen
November 3, 2026

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Trust Level
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Scored at
November 3, 2026

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Master Thesis: Early Prediction and Optimization of power, Perfomance and Area