Power Methodology Engineer, Data Center Hardware IPs
Advanced Micro Devices View all jobs
- Markham, ON
- Permanent
- Full-time
- Focuses on optimizing the energy efficiency and power delivery of high-performance computing hardware used in large-scale AI and machine learning applications
- Driving power methodology for AI-specific hardware components (like tensor cores and matrix multiplication engines) and using simulation tools (e.g., PowerArtist, PTPX) to estimate and optimize power consumption.
- Workload Optimization: Analyze the power and performance characteristics of AI, Graphics, Battery life WLs, especially specific WLs for NPUs, GPUs, and CPUs.
- Performance/Watt Optimization: Focus on maximizing performance while staying within strict power and thermal limits, which is critical for both data center and gaming applications.
- Power Optimization: Estimate and analyze power consumption at various stages of chip design (architecture, RTL, physical design).
- Analysis and modeling: Creating power models and scripts for performance/power trade-offs.
- Methodology Development: Researching, developing, and deploying methodologies and automated flows (using scripting languages like Python or Perl) to enhance power analysis efficiency.
- Collaboration: Working with other teams, including RTL, Architecture, Physical Design, Emulation, software, Firmware to ensure power requirements are met across the hardware-software stack.
- Leadership: Mentoring junior team members and providing technical leadership on complex projects.
- Extensive industry experience, with a specialization in low-power-processor architectures or power management.
- Expertise in ASIC/SoC power analysis and optimization techniques
- Working experience in dynamic and leakage power estimation, analysis, and reduction at various levels (architecture, RTL, circuit design)
- AI/ML Concepts: Familiarity with machine learning algorithms and their application to power simulation/optimization, as well as an understanding of NPU function and AI workload characteristics.
- Proficiency in hardware description languages like Verilog or VHDL, and scripting
- Strong analytical skills and experience with power analysis tools (e.g., PowerArtist, PTPX)
- Expertise in hardware description languages (Verilog, VHDL), scripting (Python), and simulation/analysis tools
- Strong analytical and problem-solving skills to tackle complex, multidisciplinary power and performance challenges. Several years of experience in dynamic and leakage power estimation, analysis, and reduction at various levels (architecture, RTL, circuit design)
- Strong scripting and automation skills, preferably in Python
- Excellent communication, presentation, and leadership skills to drive projects and collaborate effectively with cross-functional teams.
- Master's degree or PhD in a relevant field, such as Electrical or Computer Engineering, is often preferred.