Optimally profiling and tracing programs
ACM Transactions on Programming Languages and Systems (TOPLAS)
An Index-Based Approach for Similarity Search Supporting Time Warping in Large Sequence Databases
Proceedings of the 17th International Conference on Data Engineering
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
Scaling and time warping in time series querying
The VLDB Journal — The International Journal on Very Large Data Bases
Computer
Proceedings of the VLDB Endowment
Faster retrieval with a two-pass dynamic-time-warping lower bound
Pattern Recognition
The VLDB Journal — The International Journal on Very Large Data Bases
Custom floating-point unit generation for embedded systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Accelerating Dynamic Time Warping Subsequence Search with GPUs and FPGAs
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Understanding sources of ineffciency in general-purpose chips
Communications of the ACM
A Low Power Wake-Up Circuitry Based on Dynamic Time Warping for Body Sensor Networks
BSN '11 Proceedings of the 2011 International Conference on Body Sensor Networks
Designing Custom Arithmetic Data Paths with FloPoCo
IEEE Design & Test
Improving Floating-Point Performance in Less Area: Fractured Floating Point Units (FFPUs)
Journal of Signal Processing Systems
Exact and approximate algorithms for the extension of embedded processor instruction sets
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Searching and mining trillions of time series subsequences under dynamic time warping
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A Customized Processor for Energy Efficient Scientific Computing
IEEE Transactions on Computers
Accelerating subsequence similarity search based on dynamic time warping distance with FPGA
Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
Proceedings of the Conference on Design, Automation and Test in Europe
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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.