標題: | A Reconfigurable 64-Dimension K-Means Clustering Accelerator With Adaptive Overflow Control |
作者: | Du, Li Du, Yuan Chang, Mau-Chung Frank 交大名義發表 National Chiao Tung University |
關鍵字: | Machine learning;unsupervised learning;K-means;hardware accelerator;clustering;vector flow |
公開日期: | 1-Apr-2020 |
摘要: | 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 mm(2) 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. |
URI: | http://dx.doi.org/10.1109/TCSII.2019.2922657 http://hdl.handle.net/11536/154241 |
ISSN: | 1549-7747 |
DOI: | 10.1109/TCSII.2019.2922657 |
期刊: | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS |
Volume: | 67 |
Issue: | 4 |
起始頁: | 760 |
結束頁: | 764 |
Appears in Collections: | Articles |