← 研踪 PaperTrailarXiv:2511.22348 · v3
FADiff: Fusion-Aware Differentiable Optimization for DNN Scheduling on Tensor Accelerators
首次提交 2025-11-27 19:38
版本更新 2026-10-26 18:32
雷达观测 2026-10-29 00:36
摘要
Efficiently deploying Deep Neural Networks (DNNs) - particularly Large Language Models (LLMs) - on tensor accelerators is essential for achieving high throughput and energy efficiency at the edge. However, the combinatorial explosion of the joint design space spanning intra-layer mapping and inter-layer fusion renders existing approaches inadequate. In this work, we present FADiff, a gradient-based optimization framework that automatically discovers optimal mapping and fusion strategies to accelerate DNN inference. We first formulate a unified, differentiable analytical cost model that accurately estimates the energy and latency of individual layer mappings as well as diverse fusion configurations. We then combine CP-SAT-based pre-enumeration of feasible discrete candidates with augmented-Lagrangian penalties over continuous relaxations, enabling a constrained gradient-based search that efficiently navigates the vast design space to identify optimal joint strategies. Experimental results show that FADiff consistently outperforms prior methods, delivering superior optimization quality with significantly faster convergence in both energy and latency.
作者备注：Accepted to ASPDAC 2027
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v3 · 2026-10-26 18:32 · 最新观测版本
FADiff: Fusion-Aware Differentiable Optimization for DNN Scheduling on Tensor Accelerators
Efficiently deploying Deep Neural Networks (DNNs) - particularly Large Language Models (LLMs) - on tensor accelerators is essential for achieving high throughput and energy efficiency at the edge. However, the combinatorial explosion of the joint design space spanning intra-layer mapping and inter-layer fusion renders existing approaches inadequate. In this work, we present FADiff, a gradient-based optimization framework that automatically discovers optimal mapping and fusion strategies to accelerate DNN inference. We first formulate a unified, differentiable analytical cost model that accurately estimates the energy and latency of individual layer mappings as well as diverse fusion configurations. We then combine CP-SAT-based pre-enumeration of feasible discrete candidates with augmented-Lagrangian penalties over continuous relaxations, enabling a constrained gradient-based search that efficiently navigates the vast design space to identify optimal joint strategies. Experimental results show that FADiff consistently outperforms prior methods, delivering superior optimization quality with significantly faster convergence in both energy and latency.
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