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.

A traditional extensible processor with customized circuits achieves high performance at the cost of flexibility, while a dynamically extensible processor with reconfigurable fabric offers flexibility for instruction-set extensions (ISEs) but suffers from computational inefficiency. We introduce a novel architecture called Just-in-Time Customizable (JiTC) processor that reconciles the conflicting demands of performance and flexibility in extensible processors. Our key innovation is a multi-stage accelerator, called Specialized Functional Unit (SFU), that is tightly integrated in the processor pipeline. The SFU design is derived through a systematic study of a large range of representative embedded applications. The SFU can be reconfigured on per-cycle basis to support different application-specific instructions at near-ideal performance of an extensible processor. We also provide an automated compilation tool chain for JiTC processor. The experimental results confirm the efficiency and applicability of our approach. © 2013 IEEE.

This paper describes an application-specific embedded processor with instruction set extensions (ISEs) for the Dynamic Time Warping (DTW) distance measure, which is widely used in time series similarity search. The ISEs in this paper are implemented using a form of logarithmic arithmetic that offers significant performance and power/energy advantages compared to more traditional floating-point operations.