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

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 1.4x and 875x 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 2.3x 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.

Automated summary

K-Nearest Neighbor (kNN) is an efficient algorithm used in various applications, and the proposed hardware accelerator for kNN shows significant improvements in energy consumption and speed compared to state-of-the-art implementations. Approximate Computing Techniques (ACTs) further enhance the proposed architecture, accelerating the classification process and reducing energy consumption.