一句话：提出一种面向硬件加速器编译的静态内存分配方法，通过规范解搜索、空间推断和支配解消除大幅提升性能并缩小库体积。
该论文针对将机器学习模型编译到专用硬件加速器时的静态内存分配问题，提出 MiniMalloc。方法将分配过程建模为递归深度优先搜索，并将搜索限制在一类规范解上以压缩搜索空间；同时提出空间推断技术，用于剪枝不有希望的部分赋值并更有效回溯；还引入支配解检测与消除机制，避免重复或劣质方案。评估在多个基准上与先前最优方法比较，显示性能有数量级提升，且库体积显著减小。
We present a new approach to static memory allocation, a key problem that arises in the compilation of machine learning models onto the resources of a specialized hardware accelerator. Our methodology involves a recursive depth-first search that limits exploration to a special class of canonical solutions, dramatically reducing the size of the search space. We also develop a spatial inference technique that exploits this special structure by pruning unpromising partial assignments and backtracking more effectively than otherwise possible. Finally, we introduce a new mechanism capable of detecting and eliminating dominated solutions from consideration. Empirical results demonstrate orders of magnitude improvement in performance as compared to the previous state-of-the-art on many benchmarks, as well as a substantial reduction in library size.