Integrated Exploration Methodology for Data Interleaving and Data-to-Memory Mapping on SIMD Architectures
Iason Filippopoulos, Namita Sharma, Francky Catthoor, Per Gunnar Kjeldsberg, Preeti Ranjan Panda · ACM Transactions on Embedded Computing Systems · 2016
This work presents a methodology for efficient exploration of data interleaving and data-to-memory mapping options for Single Instruction Multiple Data (SIMD) platform architectures. The system architecture consists of a reconfigurable clustered scratch-pad memory and a SIMD functional unit, which performs the same operation on multiple input data in parallel. The memory accesses contribute substantially to the overall energy consumption of an embedded system executing a data intensive task. The scope of this work is the reduction of the overall energy consumption by increasing the utilization of the functional units and decreasing the number of memory accesses. The presented methodology is tested using a number of benchmark applications with holes in their access scheme. Potential gains are calculated based on the energy models, both for the processing and the memory part of the system. The reduction in energy consumption after efficient interleaving and mapping of data is between 40% and 80% for the complete system and the studied benchmarks.