UC Riverside

Processor specialization through application-specific instruction set customization can significantly improve performance while reducing energy. Due to the costs associated with semiconductor fabrication, specialized processors are only viable for products with high production volumes. The emergence of low-cost sensor-based computing products in recent years has created an urgent need to process time-series data with the utmost efficiency. Although most sensor data is fixed-point, the normalization process - an absolute necessity for highly accurate similarity search of time-series data - converts the data to floating-point in order to avoid a loss in precision. The sensors that collect time-series data are typically connected to low-power microcontrollers or RISC processors sans floating point units. The computational requirements of real-time similarity search would overwhelm such processors. To address this concern, we introduce a specialized instruction set for time-series data mining applications to a 32-bit embedded processor, yielding a 4.87x performance improvement and a 78% reduction in energy consumption compared to a highly optimized software implementation. © 2013 IEEE.