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NVIDIA

Designs the chips and software that power AI

Architecture Energy Modeling Engineer Intern

Silicon & ASIC DesignConfirmed live in the last 24 hours
InternshipShanghai, ChinaNo salary listedIn-person

About the Job

Requirements

  • Pursuing a Master of Science degree.
  • Strong coding skills, preferably in Python and C++.
  • Background in machine learning, artificial intelligence, and/or statistical modeling.
  • Interest in computer architecture and energy-efficient GPU designs.
  • Ability to formulate and analyze algorithms and comment on their runtime and memory complexities.
  • Good verbal and written English skills.

Responsibilities

  • 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 machine learning and/or statistical techniques.
  • Develop methodologies to improve the accuracy of energy models under various constraints, such as process, timing, floorplan, and layout.
  • Correlate 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 workloads run on silicon, RTL, and architectural simulators, and work with architects to fix the identified 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.

Desired Qualifications

  • Familiarity with Verilog and ASIC design principles is a plus.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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