Self-Organizing Maps (SOMs) are widely used as a data mining technique for applications that require data dimensionality reduction and clustering. Given the complexity of the SOM learning phase and the massive dimensionality of many data sets as well as their sample size in Big Data applications, high-speed processing is critical when implementing SOM approaches. This paper proposes a new hardware approach to SOM implementation, exploiting parallelization, to optimize the system’s processing time. Unlike most implementations in the literature, this proposed approach allows the parallelization of the data dimensions instead of the map, ensuring high processing speed regardless of data dimensions. An implementation with field-programmable gate arrays (FPGA) is presented and evaluated. Key evaluation metrics are processing time (or throughput) and FPGA area occupancy (or hardware resources).
|Title of host publication||2020 IEEE 31st International Conference on Application-specific Systems, Architectures and Processors (ASAP)|
|Publication status||Published - 31 Jul 2020|
- Big data
- self-organizing map