@inproceedings{scholars17374, title = {An Adaptive Hardware Architecture using Quantized HOG Features for Object Detection}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, journal = {Proceedings of 2022 IEEE International Conference on IC Design and Technology, ICICDT 2022}, pages = {113--116}, note = {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}, doi = {10.1109/ICICDT56182.2022.9933068}, year = {2022}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142756779&doi=10.1109\%2fICICDT56182.2022.9933068&partnerID=40&md5=8dbc6c28f9905b2df68cc139546542d7}, 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}, 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. {\^A}{\copyright} 2022 IEEE.}, author = {Nguyen, N.-D. and Bui, D.-H. and Hussin, F. A. and Tran, X.-T.}, isbn = {9781665459013} }