英文字典中文字典


英文字典中文字典51ZiDian.com



中文字典辞典   英文字典 a   b   c   d   e   f   g   h   i   j   k   l   m   n   o   p   q   r   s   t   u   v   w   x   y   z       







请输入英文单字，中文词皆可：


请选择你想看的字典辞典：
单词字典翻译
zweet查看　zweet　在百度字典中的解释百度英翻中〔查看〕
zweet查看　zweet　在Google字典中的解释Google英翻中〔查看〕
zweet查看　zweet　在Yahoo字典中的解释Yahoo英翻中〔查看〕

安装中文字典英文字典查询工具!


中文字典英文字典工具:
选择颜色:
输入中英文单字

































































英文字典中文字典相关资料:


  • IEEE Xplore
    IEEE Xplore, delivering full text access to the world's highest quality technical literature in engineering and technology | IEEE Xplore
  • Energon: Toward Efficient Acceleration of Transformers Using Dynamic . . .
    In recent years, transformer models have revolutionized natural language processing (NLP) and shown promising performance on computer vision (CV) tasks Despite their effectiveness, transformers’ attention operations are hard to accelerate due to the complicated data movement and quadratic computational complexity, prohibiting the real-time inference on resource-constrained edge-computing
  • Dynamic Sparse Attention for Scalable Transformer Acceleration
    Transformers are the mainstream of NLP applications and are becoming increasingly popular in other domains such as Computer Vision Despite the improvements in model quality, the enormous computation costs make Transformers difficult at deployment, especially when the sequence length is large in emerging applications Processing attention mechanism as the essential component of Transformer is
  • IEEE Transactions on Computer-Aided Design of Integrated . . . - IEEE Xplore
    The purpose of the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems is to publish papers of interest to individuals in the area of computer-aided design of integrated circuits and systems composed of analog, digital, mixed-signal, optical, or microwave components The aids include methods, models, algorithms, and man-machine interfaces for system-level, physical
  • SWAT: Scalable and Efficient Window Attention-based Transformers . . .
    Efficiently supporting long context length is crucial for Transformer models The quadratic complexity of the self-attention computation plagues traditional Transformers Sliding window-based static sparse attention mitigates the problem by limiting the attention scope of the input tokens, reducing the theoretical complexity from quadratic to linear Although the sparsity induced by window
  • CoDA: A Co-Design Framework for Versatile and Efficient Attention . . .
    Additionally, they mainly focus on the dynamic pruning of attention matrices, which requires the deployment of pre-processing units, thereby reducing overall hardware efficiency This paper presents CoDA which is an algorithm, dataflow and architecture co-design framework for versatile and efficient attention accelerators
  • Libra: A Hybrid-Sparse Attention Accelerator Featuring Multi-Level . . .
    To fully exploit the acceleration potential of hybrid sparsity, we propose Libra, an attention accelerator developed through algorithm-hardware co-design At the algorithm level, we design the bit-group-based algorithm consisting of filtered bit-group sparsification (FBS) and dynamic bit-group quantization (DBQ) to maximize the utilization of
  • ViTALiTy: Unifying Low-rank and Sparse Approximation for Vision . . .
    Vision Transformer (ViT) has emerged as a competitive alternative to convolutional neural networks for various computer vision applications Specifically, ViTs’ multi-head attention layers make it possible to embed information globally across the overall image Nevertheless, computing and storing such attention matrices incurs a quadratic cost dependency on the number of patches, limiting
  • Efficient Transformer Inference Through Hybrid Dynamic Pruning
    To overcome these challenges, we introduce a novel hybrid dynamic pruning (HDP) technique, an efficient algorithm-architecture codesign approach that accelerates transformers using head sparsity, block sparsity, and approximation to reduce computations in attention and reduce memory access
  • Vision Transformer Acceleration via a Versatile Attention Optimization . . .
    Vision Transformers (ViTs) have achieved remarkable success across various tasks However, their deployment is hindered by challenges, such as high memory requirements, long inference latency, and significant power consumption To address these challenges, existing works optimize one of the two key stages on ViT’s critical path: linear projection or self-attention Regrettably, we have





中文字典-英文字典  2005-2009