A 58.6mW 30fps Real-Time Programmable Multi-Object Detection Accelerator with Deformable Parts Models on Full HD 1920×1080 Videos
Name
2017_jssc_dpm.pdf
Size
7.57 MB
Format
Adobe PDF
Checksum (MD5)
8b22e87729e90155cebfab54d01191ad
Author(s)
Sze, Vivienne
Suleiman, Amr AbdulZahir
Zhang, Zhengdong
Alternative Title
A 58.6 mW 30 Frames/s Real-Time Programmable Multiobject Detection Accelerator With Deformable Parts Models on Full HD 1920×1080 Videos
Date Issued
January 2017
Journal
IEEE Journal of Solid State Circuits
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Suleiman, Amr, Zhang, Zhengdong, and Sze, Vivienne. "A 58.6 mW 30 Frames/s Real-Time Programmable Multiobject Detection Accelerator With Deformable Parts Models on Full HD 1920×1080 Videos." IEEE Journal of Solid State Circuits, 52 (March 2017): 844-855.© 2017 Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
This paper presents a programmable, energy-efficient, and real-time object detection hardware accelerator for low power and high throughput applications using deformable parts models, with 2x higher detection accuracy than traditional rigid body models. Three methods are used to address the high computational complexity of eight deformable parts detection: classification pruning for 33x fewer part classification, vector quantization for 15x memory size reduction, and feature basis projection for 2x reduction in the cost of each classification. The chip was fabricated in a 65 nm CMOS technology, and can process full high definition 1920 × 1080 videos at 60 frames/s without any OFF-chip storage. The chip has two programmable classification engines (CEs) for multiobject detection. At 30 frames/s, the chip consumes only 58.6 mW (0.94 nJ/pixel, 1168 GOPS/W). At a higher throughput of 60 frames/s, the CEs can be time multiplexed to detect even more than two object classes. This proposed accelerator enables object detection to be as energy-efficient as video compression, which is found in most cameras today.
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/JSSC.2017.2648820
