Architecture Energy Modeling Engineer – New College Grad 2024
Posted on 4/2/2024
NVIDIA

10,001+ employees

Designer & manufacturer of computer chips & graphics processors
Company Overview
NVIDIA is on a mission to solve the world's most stimulating technology problems – in industries ranging from gaming to scientific exploration.
AI & Machine Learning

Company Stage

N/A

Total Funding

$4.2B

Founded

1993

Headquarters

Santa Clara, California

Growth & Insights
Headcount

6 month growth

8%

1 year growth

18%

2 year growth

20%
Locations
Santa Clara, CA, USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Verilog
Python
Data Structures & Algorithms
CategoriesNew
Hardware Engineering
Computer Hardware Engineering
Requirements
  • Pursuing a BS in Electrical Engineering or Computer Engineering or related degree 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.
  • Familiarity with Verilog and ASIC design principles is a plus.
  • 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.
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.