A survey of techniques for optimizing transformer inference
Recent years have seen a phenomenal rise in the performance and applications of
transformer neural networks. The family of transformer networks, including Bidirectional …
transformer neural networks. The family of transformer networks, including Bidirectional …
A review of the optimal design of neural networks based on FPGA
C Wang, Z Luo - Applied Sciences, 2022 - mdpi.com
Deep learning based on neural networks has been widely used in image recognition,
speech recognition, natural language processing, automatic driving, and other fields and …
speech recognition, natural language processing, automatic driving, and other fields and …
M³vit: Mixture-of-experts vision transformer for efficient multi-task learning with model-accelerator co-design
Multi-task learning (MTL) encapsulates multiple learned tasks in a single model and often
lets those tasks learn better jointly. Multi-tasking models have become successful and often …
lets those tasks learn better jointly. Multi-tasking models have become successful and often …
Autorep: Automatic relu replacement for fast private network inference
The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients'
data privacy and security issues. Private inference (PI) techniques using cryptographic …
data privacy and security issues. Private inference (PI) techniques using cryptographic …
Lingcn: Structural linearized graph convolutional network for homomorphically encrypted inference
Abstract The growth of Graph Convolution Network (GCN) model sizes has revolutionized
numerous applications, surpassing human performance in areas such as personal …
numerous applications, surpassing human performance in areas such as personal …
An algorithm–hardware co-optimized framework for accelerating n: M sparse transformers
The Transformer has been an indispensable staple in deep learning. However, for real-life
applications, it is very challenging to deploy efficient Transformers due to the immense …
applications, it is very challenging to deploy efficient Transformers due to the immense …
A length adaptive algorithm-hardware co-design of transformer on fpga through sparse attention and dynamic pipelining
Transformers are considered one of the most important deep learning models since 2018, in
part because it establishes state-of-the-art (SOTA) records and could potentially replace …
part because it establishes state-of-the-art (SOTA) records and could potentially replace …
Vaqf: Fully automatic software-hardware co-design framework for low-bit vision transformer
The transformer architectures with attention mechanisms have obtained success in Nature
Language Processing (NLP), and Vision Transformers (ViTs) have recently extended the …
Language Processing (NLP), and Vision Transformers (ViTs) have recently extended the …
Via: A novel vision-transformer accelerator based on fpga
Since Google proposed Transformer in 2017, it has made significant natural language
processing (NLP) development. However, the increasing cost is a large amount of …
processing (NLP) development. However, the increasing cost is a large amount of …
Towards sparsification of graph neural networks
As real-world graphs expand in size, larger GNN models with billions of parameters are
deployed. High parameter count in such models makes training and inference on graphs …
deployed. High parameter count in such models makes training and inference on graphs …