Job Description

Locations

• Cupertino, California, United States 95014

Salary

Skills Required

• SOC power optimization automationintermediate
• power modelingintermediate
• power simulationintermediate
• power analysisintermediate
• automationintermediate
• power data analysisintermediate
• creating power metricsintermediate
• machine learningintermediate
• training data preparationintermediate
• ML-based power simulationintermediate
• ML-based power optimizationintermediate
• ML-based power modelingintermediate
• cross-functional collaborationintermediate
• methodology improvementintermediate

Required Qualifications

• A minimum of a bachelor's degree in relevant field and a minimum of 3 years of relevant industry experience. (experience, 3 years)

Preferred Qualifications

• MSEE preferred (experience)
• Familiarity with machine learning algorithms and programming (experience)
• Familiarity with scripting in Python. (experience)
• Familiarity with ASIC power analysis and optimization (experience)
• Familiarity with SOC design flow and methodology (experience)
• Familiarity with Verilog and System Verilog. (experience)
• Familiarity computer architecture, logic and circuits design (experience)
• Familiarity signal processing is a plus (experience)
• Silicon power measurement experience is a plus (experience)
• SOC power modeling, low power design and power optimization experience is a plus (experience)
• Strong communication skills are a pre-requisite as the candidate will interface with a lot of different groups (experience)

Responsibilities

• The main responsibility of this role is to drive the automation for SOC power optimization.
• - Working with SOC power team members to automate the power analysis and optimization tasks
• - Explore new methodology to analyze the power data, create power metrics to identify the optimization opportunities, drive automated approach to improve SOC power efficiency across IPs
• - Explore machine learning approaches to optimize SOC power, work with team members on training data preparation, research ML based solution for power simulation, optimization and modeling.
• - Collaborate cross-functional teams to deploy the power optimization automation to improve SOC power, review results, improve the methodology on efficiency, runtime, and support boarder scope of designs.

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