Chip Power Estimation Engineer at Nvidia
Who can apply
- Experience
- 1+ years
- Branches
- CSE
About this role
NVIDIA is seeking a passionate, creative, and highly motivated engineer to work on architectural power estimation for the world’s leading GPUs and SOCs. In this position, the responsibility includes development of advanced power models to estimate chip and board power under product driven use cases. You are expected to understand the high-level chip architecture, application use-cases, low power design techniques, process technology aspects which impact dynamic and leakage power, develop the estimation infrastructure, estimate power consumption under various scenarios. You will be working with architecture, design, synthesis, timing, circuit, and post silicon teams to accomplish your tasks. This position offers the opportunity to have real impact in a dynamic, technology-focused company impacting product lines ranging from consumer graphics to self-driving cars and the growing field of artificial intelligence. We have crafted a team of outstanding people stretching around the globe, whose mission is to push the frontiers of what is possible today and define the platform for the future of computing. At NVIDIA, our employees are passionate about parallel and visual computing. We're united in our quest to transform the way graphics are used to solve some of the most complex problems in computer science. The GPU started out as an engine for simulating human imagination, conjuring up the amazing virtual worlds of video games and Hollywood films. Today, NVIDIA’s GPU simulates human intelligence, running deep learning algorithms and acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. NVIDIA is increasingly known as “the AI computing company.” What you’ll be doing: Architecture, development and correlation of power estimation models/tools for NVIDIA's chips Help architect and develop power models for use-cases, Idle power and IO power. Chip in to design the tools based on these models and their testing methodology/infr
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