An Energy-Efficient and Scalable Deep Learning/Inference Processor With Tetra-Parallel MIMD Architecture for Big Data Applications

IEEE Transactions on Biomedical Circuits and Systems
Seong-Wook ParkHoi-Jun Yoo

Abstract

Deep Learning algorithm is widely used for various pattern recognition applications such as text recognition, object recognition and action recognition because of its best-in-class recognition accuracy compared to hand-crafted algorithm and shallow learning based algorithms. Long learning time caused by its complex structure, however, limits its usage only in high-cost servers or many-core GPU platforms so far. On the other hand, the demand on customized pattern recognition within personal devices will grow gradually as more deep learning applications will be developed. This paper presents a SoC implementation to enable deep learning applications to run with low cost platforms such as mobile or portable devices. Different from conventional works which have adopted massively-parallel architecture, this work adopts task-flexible architecture and exploits multiple parallelism to cover complex functions of convolutional deep belief network which is one of popular deep learning/inference algorithms. In this paper, we implement the most energy-efficient deep learning and inference processor for wearable system. The implemented 2.5 mm × 4.0 mm deep learning/inference processor is fabricated using 65 nm 8-metal CMOS technology for a ba...Continue Reading

References

Jun 13, 2006·Neural Computation·Geoffrey E HintonYee-Whye Teh
Mar 7, 2012·IEEE Transactions on Pattern Analysis and Machine Intelligence·Shuiwang JiKai Yu
May 2, 2012·The British Journal of General Practice : the Journal of the Royal College of General Practitioners·Mike Pringle

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Citations

Jul 31, 2016·Briefings in Bioinformatics·Seonwoo MinSungroh Yoon
Sep 12, 2018·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Ke HanChengzhi Zhang

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