Chunwei Xia

Shuoming Zhang, Jiacheng Zhao, Qiuchu Yu et al.

This survey has provided a systematic overview of the emerging field of LLM-enabled compilation by addressing several key research questions. We first answered how LLMs are being integrated by proposing a comprehensive, multi-dimensional taxonomy that categorizes works based on their Design Philosophy (Selector, Translator, Generator), LLM Methodology, their operational Level of Code Abstraction, and the specific Task Type they address. In answering what advancements these approaches offer, we identified three primary benefits: the democratization of compiler development, the discovery of novel optimization strategies, and the broadening of the compiler's traditional scope. Finally, in addressing the field's challenges and opportunities, we highlighted the critical hurdles of ensuring correctness and achieving scalability, while identifying the development of hybrid systems as the most promising path forward. By providing these answers, this survey serves as a foundational roadmap for researchers and practitioners, charting the course for a new generation of LLM-powered, intelligent, adaptive and synergistic compilation tools.

Huawei Zhang, Chunwei Xia, Zheng Wang

Language models (LMs) underpin emerging mobile and embedded AI applications like meeting and video summarization and document analysis, which often require processing multiple long-context inputs. Running an LM locally on-device improves privacy, enables offline use, and reduces cost, but long-context inference quickly hits a \emph{memory capacity wall} as the key-value (KV) cache grows linearly with context length and batch size. We present KVSwap, a software framework to break this memory wall by offloading the KV cache to non-volatile secondary storage (disk). KVSwap leverages the observation that only a small, dynamically changing subset of KV entries is critical for generation. It stores the full cache on disk, uses a compact in-memory metadata to predict which entries to preload, overlaps computation with hardware-aware disk access, and orchestrates read patterns to match storage device characteristics. Our evaluation shows that across representative LMs and storage types, KVSwap delivers higher throughput under tight memory budgets while maintaining the generation quality when compared with existing KV cache offloading schemes.