ASIC Architect

Cerebras Systems

• $150K — $200K *
Sunnyvale, CA 94087In-Person
Enterprise Technology
8 - 10 years of experience
Today
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Job Overview by Ladders

Qualifications

  • Masters/PhD in Electrical/Computer Engineering
  • 10+ years of experience in performance analysis and modeling for GPUs, CPUs, or accelerator products
  • Strong background in computer architecture and architectural trade-offs
  • Ability to develop performance models from scratch in Python or similar environments
  • Experience with micro-code performance bottlenecks and optimization techniques
  • Understanding of mapping high-level workloads to micro-architecture is desired
  • Familiarity with ML workload profiling techniques and model network architecture is preferred

Responsibilities

  • Translate high level architecture specifications into micro-architecture feature requirements
  • Develop and bring up new features in the performance/power model
  • Conduct comprehensive PPA (Power, Performance, Area) trade-offs for new architectural features
  • Extract insights for enhancing micro-architecture power efficiency
  • Profile workloads to identify bottlenecks and evaluate competitive performance for benchmarking
  • Collaborate with software teams for application-level modeling at the cluster level
  • Identify opportunities for hardware acceleration at the kernel level
Full Job Description
Responsibilities
  • Translate high level architecture spec to micro-architecture feature requirements
  • Bring up new features in the performance/power model
  • Perform comprehensive PPA trade-offs for new architectural features
  • Extract insights for new features and micro-architecture power efficiency
  • Profile workloads, identify bottlenecks and project competition performance for benchmarking
  • Engage with SW teams for end-end application level modeling at cluster level
  • Identify kernel level HW acceleration level opportunities

Qualifications
  • Masters/PhD in Electrical/Computer Engineering
  • 10+ years of experience across performance analysis and modeling across GPUs, CPUs or accelerator products
  • Strong background in computer architecture and key high level architectural trade-offs
  • Comfortable standing up new performance models from scratch in Python or similar analytical environments
  • Exposure to micro-code (kernel) performance bottlenecks and optimization techniques
  • Good understanding of how high-level workloads map to underlying micro-architecture is desired
  • Understanding of basic ML workload profiling techniques and model network architecture is preferred
* Ladders Estimates

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