A Reconfigurable 64-Dimension K-Means Clustering Accelerator With Adaptive Overflow Control

Abstract

This brief presents a novel reconfigurable $K$ -means clustering accelerator that is suitable for integration in both IoT and data center system. The high vector dimension reconfigurability and design cost reduction is achieved through vector-streaming and adaptive overflow control to adapt distance computation using as-needed precision (dynamic 16-bit fixed-point data format). A two-stage shift-bit counted comparator is proposed. It can determine most results through only turning on the shift-bit comparator (3-bit), reducing the power consumption by $7\times $ compared to the direct full dynamic range comparison. Four vectors with two cluster centroids are processed simultaneously. Up to 8-dimension cluster vectors are stored in local buffer to reduce data exchange between the main memory and the processing engine. A prototype accelerator was implemented in TSMC 65 nm. The accelerator occupied 0.26 mm2 and can support up to 64-D vector clustering. It achieved 31.2M query vectors/s with 41-mW power consumption at 250-MHz clock (cluster number: 2, vector dimension: 64) and an energy efficiency of 0.41 TOPS/W at 30 MHz clock.