LUMINA: LLM-Guided GPU Architecture Exploration via Bottleneck Analysis
GPU design space exploration (DSE) for modern AI workloads, such as Large-Language Model (LLM) inference, is challenging because of GPUs' vast, multi-modal design spaces, high simulation costs, and complex design optimization objectives (e.g. performance, power and area trade-offs). Existing automated DSE methods are often prohibitively expensive, either requiring an excessive number of exploration samples or depending on intricate, manually crafted analyses of interdependent critical paths guided by human heuristics. We present LUMINA, an LLM-driven GPU architecture exploration framework that leverage AI to enhance the DSE efficiency and efficacy for GPUs. LUMINA extracts architectural k...
| Authors | Tao Zhang, Rui Ma, Shuotao Xu et al. |
|---|---|
| Primary category | cs.AR |
| Published | 6 March 2026 |
| Last updated at source | 18 March 2026 |
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