Publikation
Hardware Architecture of Bidirectional Long Short-Term Memory Neural Network for Optical Character Recognition
Vladimir Rybalkin; Norbert Wehn; Didier Stricker; Mohammad Reza Yousefi
In: 2017 Design, Automation & Test in Europe Conference & Exhibition |. Design, Automation & Test in Europe (DATE-2017), March 27-31, Lausanne, Switzerland, IEEE, 3/2017.
Zusammenfassung
Optical Character Recognition is conversion of
printed or handwritten text images into machine-encoded text. It
is a building block of many processes such as machine translation,
text-to-speech conversion and text mining. Bidirectional Long
Short-Term Memory Neural Networks have shown a superior
performance in character recognition with respect to other types
of neural networks. In this paper, to the best of our knowledge,
we propose the first hardware architecture of Bidirectional Long
Short-Term Memory Neural Network with Connectionist Temporal
Classification for Optical Character Recognition. Based on
the new architecture, we present an FPGA hardware accelerator
that achieves 459 times higher throughput than state-of-the-art.
Visual recognition is a typical task on mobile platforms that
usually use two scenarios either the task runs locally on embedded
processor or offloaded to a cloud to be run on high performance
machine. We show that computationally intensive visual recognition
task benefits from being migrated to our dedicated hardware
accelerator and outperforms high-performance CPU in terms of
runtime, while consuming less energy than low power systems
with negligible loss of recognition accuracy.