Power Methodology and Modeling Engineer
NVIDIA
About the Role
As a member of the Architecture Energy Modeling Team, you will collaborate with Architects, ASIC Design Engineers, Low Power Engineers, Performance Engineers, Software Engineers, and Physical Design teams to study and implement energy modeling techniques for NVIDIA's next generation GPUs, CPUs and Tegra SOCs. Your contributions will help us understand energy usage in graphics and AI workloads and make improvements in architecture, design, and power management.
Key Responsibilities
- Define and implement tools and methodologies for efficient data generation from post-layout netlists to feed into data movement power analytical models.
- Develop tools and infrastructure to sanitize each metric in the model to achieve high correlation accuracy.
- Define and implement tools and methodologies for efficient integration of power models with performance tools.
- Identify runtime and memory limitations of existing flows and tools to speed up the model delivery process.
- Mine data from pre- and post-silicon performance runs to find important data paths and bottlenecks. Give feedback to design teams and improve power efficiency.
- Work with floorplan, performance, verification, and emulation methodology and infrastructure development teams to integrate data movement power models.
- Experiment with various ML techniques to answer "what-if" design questions and set proper power/energy targets for next-generation chips.
- Enable efficient storage and retrieval of data from databases.
- Enable easy visualization of data using platforms such as PowerBI, OpenSearch.
Requirements & Tech Stack
- Strong coding skills, preferably in Python, C++.
- Ability to formulate and analyze algorithms, and comment on their runtime and memory complexities.
- Understanding of VLSI, digital design, and computer architecture concepts.
- Basic understanding of fundamental concepts of power and energy consumption, estimation, and low power design.
- Basic understanding of the chip design process from RTL design to tape-out.
- Desire to bring quantitative decision-making and analytics to improve the energy efficiency of our products.
Qualifications
- Pursuing or recently completed an MS or PhD in Electrical or Computer Engineering or equivalent experience.
- Background in machine learning, AI, and/or statistical modeling is a plus.