1 citations · 3 across the 19 of their papers we have counts for
6 papers · 1 filter
Optimized Spatial Architecture Mapping Flow for Transformer Accelerators
Haocheng Xu, Faraz Tahmasebi, Ye Qiao +3
Recent innovations in Transformer-based large language models have significantly advanced the field of general-purpose neural language understanding and generation. With billions o…
Optimizing High-Level Synthesis Designs with Retrieval-Augmented Large Language Models
Haocheng Xu, Haotian Hu, Sitao Huang
High-level synthesis (HLS) allows hardware designers to create hardware designs with high-level programming languages like C/C++/OpenCL, which greatly improves hardware design prod…
MONAS: Efficient Zero-Shot Neural Architecture Search for MCUs
Ye Qiao, Haocheng Xu, Yifan Zhang +1
Neural Architecture Search (NAS) has proven effective in discovering new Convolutional Neural Network (CNN) architectures, particularly for scenarios with well-defined accuracy opt…
RSEND: Retinex-based Squeeze and Excitation Network with Dark Region Detection for Efficient Low Light Image Enhancement
Jingcheng Li, Ye Qiao, Haocheng Xu +1
Images captured under low-light scenarios often suffer from low quality. Previous CNN-based deep learning methods often involve using Retinex theory. Nevertheless, most of them can…
TG-NAS: Generalizable Zero-Cost Proxies with Operator Description Embedding and Graph Learning for Efficient Neural Architecture Search
Ye Qiao, Jingcheng Li, Haocheng Xu +1
Neural Architecture Search (NAS) is a powerful technique for discovering high-performing CNN architectures, but most existing methods rely on costly training or extensive sampling.…
MicroNAS: Zero-Shot Neural Architecture Search for MCUs
Ye Qiao, Haocheng Xu, Yifan Zhang +1
Neural Architecture Search (NAS) effectively discovers new Convolutional Neural Network (CNN) architectures, particularly for accuracy optimization. However, prior approaches often…