An Adaptive Hardware Architecture using Quantized HOG Features for Object Detection

Nguyen, N.-D. and Bui, D.-H. and Hussin, F.A. and Tran, X.-T. (2022) An Adaptive Hardware Architecture using Quantized HOG Features for Object Detection. In: UNSPECIFIED.

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Abstract

This article presents an adaptive hardware architecture for high-performance object detection using Histogram of Oriented Gradient (HOG) features in combination with Supported Vector Machines (SVM). This architecture can adapt to various bit-width representations of HOG features by using the quantization technique. The HOG features can be represented from 8 bits to 4 bits to remove the bubble in the processing pipeline and reduce the memory footprint. As a result, the overall throughput is robustly increased as the number of bits decreases. Moreover, we propose a new cell-reused strategy to speed up the system throughput and reduce memory footprint. The proposed architecture has been implemented in TSMC 65nm technology with a maximum operating frequency of 500MHz and throughput of 3.98Gbps. The total hardware area cost is about 167KGEs and 212kb SRAMs. © 2022 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 2; Conference of 2022 IEEE International Conference on IC Design and Technology, ICICDT 2022 ; Conference Date: 21 September 2022 Through 23 September 2022; Conference Code:184070
Uncontrolled Keywords: Feature extraction; Graphic methods; Memory architecture; Object detection; Object recognition; Pipeline processing systems; Static random access storage, Hardware architecture; Histogram of oriented gradient features; Histogram of oriented gradients; Memory footprint; Objects detection; Performance; Quantized histograms; Support vectors machine; Supported vector machines, Support vector machines
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 19 Dec 2023 03:23
Last Modified: 19 Dec 2023 03:23
URI: https://khub.utp.edu.my/scholars/id/eprint/17374

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