Tensor compilers play a critical role in optimizing deep neural networks (DNNs), with memory performance emerging as a key bottleneck in code generation for DNN models. Existing tensor compilers are constrained by inefficient auto-tuning algorithms. They either must deploy coarse-grained descriptions, thus miss potential optimization, or struggle with vast search spaces, rendering auto-tuning inapplicable. Tensor compilers require a more holistic optimization of memory performance to overcome these constraints.

To address this issue, we focus our optimization objective on memory performance, which allows us to design monotonic optimization methods, significantly enhancing the efficiency of auto-tuning and thus enabling auto-tuning on a fine-granularity description. Based on these observations, we propose IntelliGen, a tensor compiler with instruction-level auto-tuning and monotonic memory optimization. We design an instruction-level graph description, iGraph, and a monotonic optimization method for optimization on iGraph. Benefiting from auto-tuning techniques with fine-grained description, IntelliGen demonstrates significant speedup of up to $3.13\times$, $3.56\times$, and $16.91\times$ (averaging $1.46\times$, $1.85\times$, and $2.30\times$, respectively) on NVIDIA GPUs, AMD GPUs, and Cambricon MLUs over the most efficient existing frameworks.