An Efficient Selection-Based kNN Architecture for Smart Embedded Hardware Accelerators

• Published in: IEEE open journal of circuits and systems Vol. 2; pp. 534 - 545
• Main Authors: Younes, Hamoud, Ibrahim, Ali, Rizk, Mostafa, Valle, Maurizio
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerators; Algorithms; Approximate computing; Approximation algorithms; Computer architecture; Computer Science; Data mining; Data processing; embedded implementation; Embedded Systems; Energy consumption; energy efficiency; Field programmable gate arrays; fPGA; Hardware; hardware accelerators; High level synthesis; IP networks; k-nearest neighbor; Parallel processing; real-time processing; Registers; Sorting; tactile sensing; Embedded Implementation; Hardware Accelerators; FPGA; Approximate Computing; K-Nearest Neighbor; Tactile Sensing; High Level Synthesis; Energy Efficiency; Real-time Processing
• ISSN: 2644-1225; 2644-1225
• DOI: 10.1109/OJCAS.2021.3108835

K-Nearest Neighbor (kNN) is an efficient algorithm used in many applications, e.g., text categorization, data mining, and predictive analysis. Despite having a high computational complexity, kNN is a candidate for hardware acceleration since it is a parallelizable algorithm. This paper presents an efficient novel architecture and implementation for a kNN hardware accelerator targeting modern System-on-Chips (SoCs). The architecture adopts a selection-based sorter dedicated for kNN that outperforms traditional sorters in terms of hardware resources, time latency, and energy efficiency. The kNN architecture has been designed using High-Level Synthesis (HLS) and implemented on the Xilinx Zynqberry platform. Compared to similar state-of-the-art implementations, the proposed kNN provides speedups between <inline-formula> <tex-math notation="LaTeX">1.4\times </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">875\times </tex-math></inline-formula> with 41% to 94% reductions in energy consumption. To further enhance the proposed architecture, algorithmic-level Approximate Computing Techniques (ACTs) have been applied. The proposed approximate kNN implementation accelerates the classification process by <inline-formula> <tex-math notation="LaTeX">2.3\times </tex-math></inline-formula> with an average reduced area size of 56% for a real-time tactile data processing case study. The approximate kNN consumes 69% less energy with an accuracy loss of less than 3% when compared to the proposed Exact kNN.