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This paper presents a programmable, energy-efficient and real-time objectdetection accelerator using deformable parts models (DPM), with 2x higheraccuracy than traditional rigid body models. With 8 deformable parts detection,three methods are used to address the high computational complexity:classification pruning for 33x fewer parts classification, vector quantizationfor 15x memory size reduction, and feature basis projection for 2x reduction ofthe cost of each classification. The chip is implemented in 65nm CMOStechnology, and can process HD (1920x1080) images at 30fps without any off-chipstorage while consuming only 58.6mW (0.94nJ/pixel, 1168 GOPS/W). The chip hastwo classification engines to simultaneously detect two different classes ofobjects. With a tested high throughput of 60fps, the classification engines canbe time multiplexed to detect even more than two object classes. It is energyscalable by changing the pruning factor or disabling the parts classification.

A 58.6mW Real-Time Programmable Object Detector with Multi-Scale Multi-Object Support Using Deformable Parts Model on 1920x1080 Video at 30fps