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Found 9 papers, 6 papers with code
An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer Hybrid EfficientViT
Vision Transformers (ViTs) have achieved significant success in computer vision.
A Computationally Efficient Neural Video Compression Accelerator Based on a Sparse CNN-Transformer Hybrid Network
Video compression is widely used in digital television, surveillance systems, and virtual reality.
S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-Resolution
In this paper, we propose a double-win framework for ideal and blind SR task, named S2R, including a light-weight transformer-based SR model (S2R transformer) and a novel coarse-to-fine training strategy, which can achieve excellent visual results on both ideal and random fuzzy conditions.
ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer
To marry the best of both worlds, we further propose a new mixture of experts (MoE) framework to reparameterize MLPs by taking multiplication or its primitives as experts, e. g., multiplication and shift, and designing a new latency-aware load-balancing loss.
ViTALiTy: Unifying Low-rank and Sparse Approximation for Vision Transformer Acceleration with a Linear Taylor Attention
Unlike sparsity-based Transformer accelerators for NLP, ViTALiTy unifies both low-rank and sparse components of the attention in ViTs.
NASA: Neural Architecture Search and Acceleration for Hardware Inspired Hybrid Networks
Multiplication is arguably the most cost-dominant operation in modern deep neural networks (DNNs), limiting their achievable efficiency and thus more extensive deployment in resource-constrained applications.
ViTCoD: Vision Transformer Acceleration via Dedicated Algorithm and Accelerator Co-Design
Specifically, on the algorithm level, ViTCoD prunes and polarizes the attention maps to have either denser or sparser fixed patterns for regularizing two levels of workloads without hurting the accuracy, largely reducing the attention computations while leaving room for alleviating the remaining dominant data movements; on top of that, we further integrate a lightweight and learnable auto-encoder module to enable trading the dominant high-cost data movements for lower-cost computations.
ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks
To this end, this work advocates hybrid NNs that consist of both powerful yet costly multiplications and efficient yet less powerful operators for marrying the best of both worlds, and proposes ShiftAddNAS, which can automatically search for more accurate and more efficient NNs.
Max-Affine Spline Insights Into Deep Network Pruning
In this paper, we study the importance of pruning in Deep Networks (DNs) and the yin & yang relationship between (1) pruning highly overparametrized DNs that have been trained from random initialization and (2) training small DNs that have been "cleverly" initialized.
