苹果System EE - EE/HW Intern (Suzhou)

- EE/HW development and bring up - SW development - Hands on Prototype bring up and debugging - Failure analysis

- EE/HW development and bring up - SW development - Hands on Prototype bring up and debugging - Failure analysis

NVIDIA is developing processor and system architectures that accelerate deep learning and high-performance computing applications. We are looking for an intern deep learning system performance architect to join our AI performance modelling, analysis and optimization efforts. In this position, you will have a chance to work on DL performance modelling, analysis, and optimization on state-of-the-art hardware architectures for various LLM workloads. You will make your contributions to our dynamic technology focused company. What you’ll be doing: • Analyze state of the art DL networks (LLM etc.), identify and prototype performance opportunities to influence SW and Architecture team for NVIDIA's current and next gen inference products. • Develop analytical models for the state of the art deep learning networks and algorithm to innovate processor and system architectures design for performance and efficiency. • Specify hardware/software configurations and metrics to analyze performance, power, and accuracy in existing and future uni-processor and multiprocessor configurations. • Collaborate across the company to guide the direction of next-gen deep learning HW/SW by working with architecture, software, and product teams.

GPU System Architect team’s work scope covers whole GPU pipeline(graphics, compute pipeline, memory system) and multi GPU, CPU and CPU interconnection, which provides good opportunity to deeply learn the latest cross unit new features in the new GPU architectures. The team works as the safety net of the chip. We catch function bugs in the HW by randomly generating tests and running them in various pre-silicon full chip platforms and debugging the failures. This works provides a good full chip view of GPU and has a big space to innovate. What you’ll be doing: • Get familiar with various GPU workload’s composition • Learn about what’s the usual feature metrics for GPU workload • Design and implement inventive solution to efficiently extract features from GPU workload • Verify the solution using direct and random GPU workload • Design and implement inventive solution simplify GPU workload while keeping the required features • Design and implement inventive solution to generate GPU workload according to required features • Design and implement inventive solution to generate GPU workload which has the same feature with a given test and randomize other (required) features • Thoroughly verify the solution on GPU functional simulator/full chip RTL/emulation/silicon platform. • Provide detailed and organized documentation and report out for the project.