Kyle Liu - NYU Processor Design Documentation

Week of 9 September 2024

Project Work:

The VIP processor design team commenced the project with a kickoff meeting to establish a shared vision. Team members discussed high-level goals, including performance, power consumption, and integration challenges. By the end of the week, a comprehensive list of requirements was created, serving as a roadmap for the project.
A timeline was drafted to outline key milestones and deliverables for the project. This timeline included deadlines for design, implementation, and testing phases. The team felt confident as they laid the groundwork for a structured approach moving forward.

In the second week, the team focused on exploring different architectural options for the L1 instruction cache. They analyzed various configurations, such as direct-mapped, set-associative, and fully associative caches. Each option was evaluated based on its impact on speed, complexity, and power consumption.
Team members engaged in collaborative brainstorming sessions to weigh the pros and cons of each architectural choice. This collaborative environment fostered creativity and allowed for a diverse range of ideas to surface. Ultimately, the team selected a hybrid approach that balanced performance with practical implementation challenges.

During the third week, the team continued to plan out the L1 instruction cache efforts.

As the week progressed, the team collaborated closely to discuss the implications of their findings on overall processor design. Regular updates ensured that everyone was aligned on the direction of refinements. By the end of the week, the team had a clearer path forward, backed by solid data-driven insights.

Important instruction cache work was discussed with Core team member Xingzhi Dai and James.

The fourth week centered on performance analysis, utilizing advanced simulation tools to evaluate the cache’s effectiveness under various workloads. Key metrics, such as cache hit rates and access latencies, were rigorously analyzed to identify potential bottlenecks. The team recognized that even small optimizations could lead to significant performance improvements.