Paper Info
Title
FTRANS: Energy-Efficient Acceleration of Transformers using FPGA
Abstract
In natural language processing (NLP), the "Transformer" architecture was proposed as the first transduction model replying entirely on self-attention mechanisms without using sequence-aligned recurrent neural networks (RNNs) or convolution, and it achieved significant improvements for sequence to sequence tasks. The introduced intensive computation and storage of these pre-trained language representations has impeded their popularity into computation and memory constrained devices. The field-programmable gate array (FPGA) is widely used to accelerate deep learning algorithms for its high parallelism and low latency. However, the trained models are still too large to accommodate to an FPGA fabric. In this paper, we propose an efficient acceleration framework, Ftrans, for transformer-based large scale language representations. Our framework includes enhanced block-circulant matrix (BCM)-based weight representation to enable model compression on large-scale language representations at the algorithm level with few accuracy degradation, and an acceleration design at the architecture level. Experimental results show that our proposed framework significantly reduce the model size of NLP models by up to 16 times. Our FPGA design achieves 27.07× and 81 × improvement in performance and energy efficiency compared to CPU, and up to 8.80× improvement in energy efficiency compared to GPU.
Year
DOI
Venue
2020
10.1145/3370748.3406567
ISLPED '20: ACM/IEEE International Symposium on Low Power Electronics and Design Boston Massachusetts August, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-7053-0
7
PageRank 
References 
Authors
0.50
0
10
Name
Order
Citations
PageRank
Bingbing Li16513.34
Santosh Pandey2182.67
haowen fang3214.59
Yanjun Lyv470.50
Ji Li59710.87
Jieyang Chen6122.58
Mimi Xie781.88
Lipeng Wan892.55
Hang Liu9274.94
Caiwen Ding1014226.52