At NVIDIA, we pride ourselves in having energy efficient products. We believe that continuing to maintain our products' energy efficiency compared to the competition is key to our continued success. Our team is responsible for researching, developing, and deploying methodologies to help NVIDIA's products become more energy efficient; and is responsible for building energy models that integrate into architectural simulators, RTL simulation, and emulation platforms. Key responsibilities include developing techniques to model, analyze, and reduce the power consumption of NVIDIA GPUs.
As a member of the Power Modeling, Methodology, and Analysis Team, you will collaborate with Architects, Performance Engineers, Software Engineers, ASIC Design Engineers, and Physical Design teams to study and implement energy modeling techniques for NVIDIA's next-generation GPUs and Tegra SOCs. Your contributions will help us gain early insight into the energy consumption of graphics and artificial intelligence workloads, and will allow us to influence architectural, design, and power management improvements.
What you’ll be doing
Work with architects and performance architects to develop an energy-efficient GPU.
Develop methodologies and workflows to select and run a wide variety of workloads to train models using ML and/or statistical techniques.
Develop methodologies to improve the accuracy of energy models under various constraints, such as, process, timing, floorplan and layout.
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Correlate the predicted energy from models created at different stages of the design cycle, with the goal of bridging early estimates to silicon.
Develop tools to debug energy inefficiencies observed in various workloads run on silicon, RTL and architectural simulators. Work with architects to fix the identified energy inefficiencies.
Work with performance, verification and emulation methodology and infrastructure development teams to integrate energy models into their platforms.
Prototype new architectural features, create an energy model, and analyze the system impact.
What we need to see
Pursuing MS degree
Strong coding skills, preferably in Python, C++.
Background in machine learning, AI, and/or statistical modeling.
Interest in computer architecture and energy-efficient GPU designs.
Familiarity with Verilog and ASIC design principles is a plus.
Ability to formulate and analyze algorithms, and comment on their runtime and memory complexities.
Desire to bring quantitative decision-making and analytics to improve the energy efficiency of our products.
Good verbal/written English and interpersonal skills.
With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you! NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
