דרושים Gpu Ai Compute Architect ב-אינטל ב-אירלנד

Responsibilities:

• Conduct workload analysis and performance debugging to identify bottlenecks and drive resolution through hardware and/or software fixes.
• Evaluate AI-GPU hardware architecture and influence the product roadmap through a deep understanding of AI algorithms, customer needs, and software frameworks.
• Develop and enhance internal performance analysis tools.
• Develop highly optimized GPU kernels and software stacks, collaborating with partner teams to deliver high-performance solutions.
• Provide a comprehensive view of solutions and support both pre- and post-silicon activities.
• Collaborate with experts to analyse next-generation requirements and guide research and academic partnerships.

Minimum Qualifications:

• MS in Computer Engineering, Computer Science, Electrical Engineering, or Mathematics.
• 2-3 years of experience in GPU/CPU architecture for AI workloads.
• Proficient in Python, C/C++.Good data analysis and presentation skills.

Preferred Qualifications:

• Knowledge of AI and deep learning, including Large Language Models (LLM) and Stable Diffusion.
• Experience in performance analysis/performance debugging.
• Experience in building analytical and/or simulation-based performance models.
• Knowledge/experience in CPU, GPU, or memory design/architecture, and/ormicroarchitecture/RTL/design/processtechnologies.
• An aptitude to learn new things quickly in this fast-changing domain and flexibility to work on any mix of the above roles as needed.

This role is ideal for candidates who are eager to learn about AI hardware, possess strong collaboration skills, and can thrive in a fast-paced hardware/software development environment. Requirements listed would be obtained through a combination of industry-relevant job experience, internship experiences, and/orschoolwork/classes/research.

• Conducts design and development to build and optimize AI software.
• Designs, develops, and optimizes for AI frameworks (e.g., OpenVINO) and to contribute to external frameworks (e.g., TensorFlow, PyTorch).
• Implements various distributed algorithms such as model/data parallel frameworks, parameter servers, dataflow based asynchronous data communication in machine learning, and/or deep learning frameworks.
• Transforms computational graph representation of neural network model, and develops machine learning and/or deep learning primitives in mathematical libraries.
• Profiles distributed deep learning models to identify performance bottlenecks and proposes solutions across individual component teams.
• Optimizes code for various computing hardware backends, and interacts with machine learning and/or deep learning researchers, and utilizing experience with machine learning and/or deep learning frameworks.

Minimum qualifications are required to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates.

Preferred Qualifications:

• Bachelor/Masters in Computer science or equivalent.
• At least 3 years of experience in programming- Excellent C++ programming skills.
• Experience with C/Python/Bash/Cmake.
• Experience with any Continuous Integration system.
• Experience with Git/GitHub and cooperative development including code review and Git flow.
• Familiarity with OpenVINO framework, LLVM, MLIR, HPC, DL, ML, PyTorch, ONNX is nice to have.

Requirements listed would be obtained through a combination of industry relevant job experience, internship experiences and orschoolwork/classes/research.

Responsibilities:

• Conduct workload analysis and performance debugging to identify bottlenecks and drive resolution through hardware and/or software fixes.

• Evaluate AI-GPU hardware architecture and influence the product roadmap through a deep understanding of AI algorithms, customer needs, and software frameworks.

• Develop and enhance internal performance analysis tools.

• Develop highly optimized GPU kernels and software stacks, collaborating with partner teams to deliver high-performance solutions.

• Provide a comprehensive view of solutions and support both pre- and post-silicon activities.

• Collaborate with experts to analyse next-generation requirements and guide research and academic partnerships.

• MS in Computer Engineering, Computer Science, Electrical Engineering, or Mathematics.

• 2-3 years of experience in GPU/CPU architecture for AI workloads.

• Proficient in Python, C/C++.

• Good data analysis and presentation skills.

• Knowledge of AI and deep learning, including Large Language Models (LLM) and Stable Diffusion.

• Experience in performance analysis/performance debugging.

• Experience in building analytical and/or simulation-based performance models.

• Knowledge/experience in CPU, GPU, or memory design/architecture, and/ormicroarchitecture/RTL/design/processtechnologies.

• An aptitude to learn new things quickly in this fast-changing domain and flexibility to work on any mix of the above roles as needed.

This role is ideal for candidates who are eager to learn about AI hardware, possess strong collaboration skills, and can thrive in a fast-paced hardware/software development environment. Requirements listed would be obtained through a combination of industry-relevant job experience, internship experiences, and/orschoolwork/classes/research.

Key Responsibilities:

• As an AI HW/SW Co-Design Modelling Engineer, you'll be an integral part of a dynamic team developing NPU IP Pre-Silicon Simulation software models and tools for Intel's SoC designs.

• Collaborate with multi-disciplinary engineers and architects to model future Intel NPU hardware IP, crafting virtual platforms for early software bring-up and robust hardware validation environments.

• Gain a broad understanding of how HW-based acceleration products are built and learn why these products are the real engine behind the AI revolution.

• InnovativeEnvironment: Immerseyourself in cutting-edge virtual platform modelling technology, using platforms such as Intel Simics and state-of-the-art UVM hardware validation methodologies.

• Rapid SkillEnhancement: Expandyour existing expertise through advanced software engineering techniques and embedded software development practices.

• CollaborativeExcellence: Workalongside RTL designers, validation engineers, and SW/FW and OS driver developers to deliver world-class products

• Architectural Influence: Grow your AI development skills and contribute to architectural and technical deliverables while adhering to sound software engineering principles.

• Career Growth: We will invest in you, helping you to sustain and reach your long-term career goals focusing both on hard and soft skill development.

Minimum Qualifications:

• Master/Bachelor of Science in Electrical Engineering, Computer Science or relevant technologydegree/qualificationwith 5+ years of applicable industry experience.

• SoftwareProficiency: Strongbackground in object-oriented programming, C++,Python, Software Engineering

• Linux SoftwareDevelopment: Developmentand debug experience is a must.

• Embedded HardwareDesign: Proficiencywith VHDL/Verilog, SystemVerilog for simulation and synthesis is essential.

• Strong AIFundamentals: Understandingof underlying principles such as dot product (multiply and accumulate), activation functions, convolution, etc., and how they relate to, and enable, AI network topologies.

• Familiarity with Git and CI tools: Git for source code management, and experience with CI/CD and automated software/hardware regression testing are highly advantageous.

Preferred Qualifications:

• Virtual PlatformModelling: Hands-onexperience with Intel Simics or similar virtual platform modelling and simulation environments.

• Multithreaded ApplicationDevelopment: Expertisein C/C++.

• HW Validation Methodology: Experience beyond basic HDLs such as Verilog and VHDL.,UVM for hardware verification

• Embedded SW, Firmware, and/or SystemValidation: Debugand signoff experience.

• Numerics and AI DataProcessing: Understandingof floating point datatypes, type conversion, commutativity, and precision in the context of AI data processing structures such as tensors and blobs.

• Familiarity with SoCArchitecture: System-levelunderstanding of data flows and memory management, including simulation of HW components and functions such as registers, data/memory interfaces, MMUs, CPUs, DSPs, NoCs, interrupts, and CPR (clock, power, reset).

Requirements listed would be obtained through a combination of industry-relevant job experience, internship experiences, and/orschoolwork/classes/research.On-the-job training will be given in these areas as you take the next step in your career with our team.

Key Responsibilities:

• Design and prototype advanced compute arithmetic units (e.g., MAC arrays, ALUs) for GPUs targeting AI and deep learning workloads.

• Develop and optimize GPU microarchitectures to enhance performance, energy efficiency, and scalability for AI-specific applications.

• Create and refine RTL implementations to validate and benchmark new design concepts.

• Conduct detailed performance modelling and analysis to identify bottlenecks and propose innovative solutions for next-generation GPU designs.

• Produce comprehensive power and area estimates for proposed designs, enabling informed trade-off analysis and decision-making.

• Collaborate with cross-functional teams, including software, hardware, and machine learning experts, to align architecture design with application requirements.

• Research and integrate emerging technologies and methodologies in GPU compute design for AI workloads.

• Lead the evaluation of design trade-offs in terms of performance, area, and power metrics.

• Drive innovation in custom compute unit design, ensuring compatibility with broader GPU pipeline architecture.

Required:

• Master's or Ph.D. in Electrical Engineering, Computer Engineering, Computer Science, or a related field.

• 5 + years of experience

• Proven related experience in GPU/ASIC architecture design, with a focus on compute arithmetic via course work or relevant projects.

• Expertise in microarchitecture design and RTL coding (e.g.,SystemVerilog).Strongunderstanding of GPU pipelines, parallel computing concepts, and AI/ML workloads.

• Proven experience in designing and optimizing MAC arrays, ALUs, or similar compute units.

• Solid knowledge of hardware modelling and simulation tools (e.g., VCS, Synopsys, ModelSim).Experience in producing and interpreting power and area estimates for complex hardware designs.

• Proficiency in performance analysis tools and techniques.

• Strong problem-solving skills with the ability to innovate and think out of the box.

Preferred:

• Familiarity with high-level synthesis (HLS) tools and methodologies.

• Background in machine learning algorithms and their hardware acceleration.

• Understanding of power optimization techniques and methodologies for compute-intensive hardware.

• Requirements listed would be obtained through a combination of industry relevant job experience, internship experiences and orschoolwork/classes/research.