A 58.6mW Real-Time Programmable Object Detector with Multi-Scale Multi-Object Support Using Deformable Parts Model on 1920x1080 Video at 30fps
Name
2016_VLSI_DPM.pdf
Size
1.1 MB
Format
Adobe PDF
Checksum (MD5)
4de66c6231c807a1032f753cb9b97a0d
Author(s)
Suleiman, Amr AbdulZahir
Zhang, Zhengdong
Sze, Vivienne
Date Issued
June 2016
Journal
2016 IEEE Symposium on VLSI Circuits (VLSI-Circuits)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Suleiman, Amr, Zhengdong, Zhang, and Vivienne Sze. "A 58.6mW Real-Time Programmable Object Detector with Multi-Scale Multi-Object Support Using Deformable Parts Model on 1920x1080 Video at 30fps." In 2016 IEEE Symposium on VLSI Circuits (VLSI-Circuits), Digest of Technical Papers, held 15-17 June 2016, Honolulu, HI. IEEE.
Version
Author's final manuscript
Abstract
This paper presents a programmable, energy-efficient and real-time object detection accelerator using deformable parts models (DPM), with 2× higher accuracy than traditional rigid body models. With 8 deformable parts detection, three methods are used to address the high computational complexity: classification pruning for 33× fewer parts classification, vector quantization for 15× memory size reduction, and feature basis projection for 2× reduction of the cost of each classification. The chip is implemented in 65nm CMOS technology, and can process HD (1920×1080) images at 30fps without any off-chip storage while consuming only 58.6mW (0.94nJ/pixel, 1168 GOPS/W). The chip has two classification engines to simultaneously detect two different classes of objects. With a tested high throughput of 60fps, the classification engines can be time multiplexed to detect even more than two object classes. It is energy scalable by changing the pruning factor or disabling the parts classification.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/VLSIC.2016.7573528
