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

TL;DR

This paper introduces a low-power, real-time object detection accelerator using deformable parts models that achieves high accuracy and supports multi-object detection on 1080p video at 30fps.

Contribution

It presents a programmable, energy-efficient DPM-based accelerator capable of real-time HD video processing with innovative methods to reduce computational complexity and memory usage.

Findings

Processes HD video at 30fps with 58.6mW power consumption.

Achieves 2x higher accuracy than rigid models.

Supports multi-object detection with high throughput.

Abstract

This paper presents a programmable, energy-efficient and real-time object detection accelerator using deformable parts models (DPM), with 2x 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 33x fewer parts classification, vector quantization for 15x memory size reduction, and feature basis projection for 2x reduction of the cost of each classification. The chip is implemented in 65nm CMOS technology, and can process HD (1920x1080) 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.