Architecture Energy Modeling Engineer - New College… at NVIDIA · US

Summary: NVIDIA is a leading technology company focused on AI and computing innovation. They are seeking an Architecture Energy Modeling Engineer to enhance energy efficiency in their GPU products through the development of machine learning-based power models and collaboration with various engineering teams. Responsibilities: - Work with architects, designers, and performance engineers to develop an energy-efficient GPU - Identify key design features and workloads for building Machine Learning based unit power/energy models - Develop and own methodologies and workflows to train models using ML and/or statistical techniques - Improve the accuracy of trained models by using different model representations, objective functions, and learning algorithms - Develop methodologies to estimate data movement power/energy accurately - Correlate the predicted energy from models built at different stages of the design cycle, with the goal of bridging early estimates to silicon - Work with performance infrastructure teams to integrate power/energy models into their platforms to enable combined reporting of performance and power for various workloads - Develop tools to debug energy inefficiencies observed in various workloads run on silicon, RTL, and architectural simulators. Identify and suggest solutions to fix the energy inefficiencies - Prototype new architectural features, build an energy model for those new features, and analyze the system impact - Identify, suggest, and/or participate in studies for improving GPU perf/watt Required Qualifications: - Pursuing or recently completed a MS or PhD in Electrical Engineering, Computer Engineering, Computer Science or equivalent experience - Strong coding skills, preferably in Python, C++ - Background in machine learning, AI, and/or statistical modeling - Background in computer architecture and interest in energy-efficient GPU designs - Ability to formulate and analyze algorithms, and comment on their runtime and memory complexities - Basic understanding of fundamental concepts of energy consumption, estimation, and low power design - Desire to bring quantitative decision-making and analytics to improve the energy efficiency of our products - Good verbal/written communication and interpersonal skills Preferred Qualifications: - Familiarity with Verilog and ASIC design principles is a plus Required Skills: Python, C++, Machine Learning, Artificial Intelligence, Statistical Modeling, Computer Architecture, Energy Consumption Estimation, Low Power Design, Verilog, ASIC Design Principles, Algorithm Analysis Benefits: Equity, Benefits