# The Championship Simulator: Architectural Simulation for Education and Competition

arXiv: https://arxiv.org/abs/2210.14324 · HTML: https://mlsanity.com/paper/2210.14324/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2022.

## Built on by (most cited first, 15 of 15)

- [ArchAgent: Agentic AI-driven Computer Architecture Discovery](https://mlsanity.com/paper/2602.22425/) (2026)
- [Agentic Architect: An Agentic AI Framework for Architecture Design Exploration and Optimization](https://mlsanity.com/paper/2604.25083/) (2026)
- [Attention, Distillation, and Tabularization: Towards Practical Neural Network-Based Prefetching](https://mlsanity.com/paper/2401.06362/) (2023)
- [Memory Access Characterization of Large Language Models in CPU Environment and its Potential Impacts](https://mlsanity.com/paper/2506.01827/) (2025)
- [Athena: Synergizing Data Prefetching and Off-Chip Prediction via Online Reinforcement Learning](https://mlsanity.com/paper/2601.17615/) (2026)
- [ArchAgent v2: A Case Study with the Data Prefetching Championship](https://mlsanity.com/paper/2608.09874/) (2026)
- [A Two Level Neural Approach Combining Off-Chip Prediction with Adaptive Prefetch Filtering](https://mlsanity.com/paper/2403.15181/) (2024)
- [CacheMind: From Miss Rates to Why -- Natural-Language, Trace-Grounded Reasoning for Cache Replacement](https://mlsanity.com/paper/2602.12422/) (2026)
- [Machine Learning-Driven Intelligent Memory System Design: From On-Chip Caches to Storage](https://mlsanity.com/paper/2603.14583/) (2026)
- [ReLMXEL: Adaptive RL-Based Memory Controller with Explainable Energy and Latency Optimization](https://mlsanity.com/paper/2603.17309/) (2026)
- [PAI: Fast, Accurate, and Full Benchmark Performance Projection with AI](https://mlsanity.com/paper/2603.19330/) (2026)
- [AgentDSE: Reasoning-Augmented Architectural Design Space Exploration](https://mlsanity.com/paper/2606.21836/) (2026)
- [On the Limits of Machine-Learned Ranking for Modern Microarchitectural Policies](https://mlsanity.com/paper/2608.01041/) (2026)
- [PerfReasoning: How Well Do LLMs Reason on Hardware Performance?](https://mlsanity.com/paper/2609.04476/) (2026)
- [DuoDiscover: Scaling Autonomous Discovery Beyond the Evaluation Bottleneck](https://mlsanity.com/paper/2610.17184/) (2026)

## Abstract (arXiv)

Recent years have seen a dramatic increase in the microarchitectural complexity of processors. This increase in complexity presents a twofold challenge for the field of computer architecture. First, no individual architect can fully comprehend the complexity of the entire microarchitecture of the core. This leads to increasingly specialized architects, who treat parts of the core outside their particular expertise as black boxes. Second, with increasing complexity, the field becomes decreasingly accessible to new students of the field. When learning core microarchitecture, new students must first learn the big picture of how the system works in order to understand how the pieces all fit together. The tools used to study microarchitecture experience a similar struggle. As with the microarchitectures they simulate, an increase in complexity reduces accessibility to new users. In this work, we present ChampSim. ChampSim uses a modular design and configurable structure to achieve a low barrier to entry into the field of microarchitecural simulation. ChampSim has shown itself to be useful in multiple areas of research, competition, and education. In this way, we seek to promote access and inclusion despite the increasing complexity of the field of computer architecture.