Contributing to power estimation models and tools for GPU products and systems like NVIDIA DGX.
Early GPU & System Architecture exploration with focus on energy efficiency and TCO improvements at GPU and Datacenter level.
Helping with Performance vs Power Analysis for NVIDIA future product lineup.
Deploying machine learning techniques to develop highly accurate power and performance models of our GPUs, CPUs, Switches, and platforms.
Understanding the workload characteristics for GenAI/HPC workloads at Datacenter Scale (multi-GPU) to drive new HW/SW features for Perf@Watt improvements.
Modeling & analysis of cutting-edge technologies like high speed & high-density interconnects.

Requirements

Pursuing or recently completed a Bachelors or Masters in Electrical Engineering, Computer Engineering, or equivalent experience
Knowledge of energy efficient chip design fundamentals and related tradeoffs.
Familiarity with low power design techniques such as multi-VT, Clock gating, Power gating, and Dynamic Voltage-Frequency Scaling (DVFS).
Understanding of processors (GPU is a plus), system-SW architectures, and their performance/power modeling techniques.
Proficiency with Python and data analysis packages like: Pandas, NumPy, PyTorch.
Familiarity with performance monitors/simulators used in modern processor architectures.

Benefits

equity
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Applicant Tracking System Keywords

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Hard Skills & Tools

power estimation modelsenergy efficiencyTCO improvementsperformance vs power analysismachine learning techniquespower and performance modelsworkload characteristicshigh speed interconnectshigh-density interconnectslow power design techniques