Hardware accelerators such as GPUs, NPUs, and FPGAs are essential to meeting AI’s computational demands. With the proliferation of heterogeneous devices across cloud and edge, various model optimization techniques adapt to diverse hardware characteristics through operator transformations and structural modifications. Accurate, efficient latency prediction enables rapid selection of optimal strategies across hardware backends. Many existing methods treat hardware as a black-box executor, directly regressing latency without explicitly modeling the intricate interactions between neural network (NN) structures and device-specific execution behaviors. To address these challenges, we introduce a new modeling perspective that captures the interaction between neural architectures and hardware execution. To capture device-specific characteristics, we propose two complementary modeling strategies. The Device Behavior Signature Selector (DBSel) characterizes hardware execution behavior by selectively probing a small set of representative architectures, forming a compact, workload-driven profile. In parallel, we construct capability vectors that capture the hierarchical memory of each device and compute characteristics, providing a structured abstraction of its architectural capacity. To unify both behavioral and structural views, we introduce the Hardware–Operation Dialogue Module (HODM), which models fine-grained interactions between neural operators and hardware properties. Together, these components empower CloserToMe to deliver accurate and transferable latency predictions across unseen and diverse platforms.

Jiayi Tuo, Cheng Tang, Zihan Wang, Chenyue Zhou, Yao Li, Yanbiao Ma, Chao Wang, Wei Dai, Mingxuan Wang, Shitong Qin, Ziwei Zhao. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Image dehazing aims to recover clear images from hazy counterparts; however, current deep learning methods tend to rely on low-frequency information while neglecting multi-frequency integration, limiting their performance. To address this, we introduce Spectral Enhanced Tuning (SET), a modular framework composed of lightweight, plug-and-play components that can be easily integrated into existing dehazing networks to improve performance via efficient multi-frequency feature fusion. The framework features a Frequency Spectrum Decoder (FSD) that utilizes localized windows for high-frequency details and average pooling for low-frequency global features, improving efficiency over traditional global processing. Additionally, the Dual-Domain Guided Attention (DDGA) computes importance maps across frequency and spatial domains, surpassing simple concatenation or addition, while the Direction-Aware Convolution Module (DAConv) captures spatial details for better reconstruction. Experimental results demonstrate that SET achieves significant dehazing performance gains with negligible increases in parameters and computational cost, validating its efficiency and effectiveness.