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.

MS (or equivalent experience) with proven experience or PhD in related fields
6+ years of 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
Desire to bring quantitative decision-making and analytics to improve the energy efficiency of our products
Good verbal/written communication and interpersonal skills.

Senior Architecture Energy Modeling Engineer

Key skills