Summary
Recent advancements in large machine learning models have demonstrated that increasing the number of parameters enhances computational precision and unlocks capabilities once deemed unattainable. This trend is exemplified by the rapid growth in model sizes, for instance, GPT-3 contained 175 billion parameters, while GPT-4 reportedly utilizes up to 1.8 trillion. This trajectory is expected to continue in the foreseeable future. However, the explosive growth in model size presents two major challenges for computer architecture and systems research: prolonged simulation times, which can extend fr
