Master Thesis: AI-Guided Power Estimation and RTL Optimization

Technology, Data & Digital · Software & Web Development · Embedded Systems · Software Engineering

In short

Ericsson in Lund is offering a Master's thesis opportunity for university students to research how AI can assist in reducing RTL power consumption. The project involves analyzing RTL, activity data, and power reports to identify hotspots and propose optimizations, with a focus on front-end and RTL power estimation.

Responsibilities

Learn about and help define representative use cases for RTL power estimation.
Establish a baseline power-estimation flow with guidance from the thesis supervisors.
Analyze switching, gate, and memory activity together with power reports.
Explore how AI can identify power hotspots and propose RTL-level optimizations.
Implement and validate selected alternatives through simulation, synthesis, and power estimation.
Compare the results for dynamic power, area, timing, functional correctness, and engineering effort.
Document your methodology, experiments, results, and conclusions clearly.

Requirements

Knowledge in digital hardware design, computer architecture, electronics, embedded systems or a related field.
Basic knowledge of Verilog or SystemVerilog; experience from university assignments or personal projects is welcome.
Interest in artificial intelligence, machine learning, or AI-assisted engineering workflows.
Eagerness to learn and explore new concepts.
Ability to analyze data, communicate findings, and document work clearly.
A structured and self-motivated approach to academic project work.

Skills

VerilogSystemVerilogmachine learningartificial intelligence

#RTL optimization#power estimation#artificial intelligence#machine learning#master thesis#Ericsson#Lund

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