Abstract
Power efficiency is becoming a critical aspect of IoT devices. In this paper, we present a compact object-detection coprocessor with multiple cores for multi-scale/type classification. This coprocessor is capable to process scalable block size for multi-shape detection-window and can be compatible with the frame-image sizes up to 2048 × 2048 for multi-scale classification. A memory-reuse strategy that requires only one dual-port SRAM for storing the feature-vector of one-row blocks is developed to save memory usage. Eventually, a prototype platform is implemented on the Intel DE4 development board with the Stratix IV device. The power consumption of each core in FPGA is only 80.98 mW.
Highlights
A Multi-Core Object Detection Coprocessor forPeng Xu 1,† , Zhihua Xiao 1,† , Xianglong Wang 1 , Lei Chen 2 , Chao Wang 3,4 and Fengwei An 1,2,5, *
Real-time processing ability is required by multiple tasks such as auto-drive, Internet of Things (IoT) systems, security systems, and so on
We propose a multi-core object detection coprocessor for multi-scale/type classification considering the speed-power-accuracy tradeoff within the Histogram of Orientated Gradient (HOG) and Support Vector Machine (SVM) framework as shown in is suitable for vehicle detection
Summary
Peng Xu 1,† , Zhihua Xiao 1,† , Xianglong Wang 1 , Lei Chen 2 , Chao Wang 3,4 and Fengwei An 1,2,5, *. Engineering Research Center of Integrated Circuits for Next-Generation Communications, Ministry of Education, Southern University of Science and Technology, Shenzhen 518055, China. Received: 20 September 2020; Accepted: October 2020; Published: October 2020
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