University of California, Los Angeles (Anderson)
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.