A novel parallel multiplier using spiking neural P systems with dendritic delays
| Autor: | Giovanny Sanchez, Thania Frias, Gonzalo Duchen, Carlos Díaz, Karina Toscano, Hector Perez |
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| Rok vydání: | 2017 |
| Předmět: |
Divide and conquer algorithms
Spiking neural network 0209 industrial biotechnology Computer science Cognitive Neuroscience 02 engineering and technology Computer Science Applications 020901 industrial engineering & automation medicine.anatomical_structure Artificial Intelligence 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Multiplication Multiplier (economics) Neuron Algorithm Electronic circuit |
| Zdroj: | Neurocomputing. 239:113-121 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2017.02.009 |
| Popis: | High performance spiking neural multiplier.Parallel input data processing in SN P systems.Scalable parallel architecture based on SN P systems. In the last 10 years, there has been a considerable increase in the number of studies on the development of multiplier circuits based on spiking neural P systems with the aim of taking advantage of their intrinsic distributed parallel computing characteristics. Nevertheless, these efforts have had difficulties in adequately exploiting parallel data processing because they are designed to process the input data using a sequential protocol and thus suffer from the resulting increase in processing time. This paper develops a novel parallel multiplier that is based on spiking neural P systems and is capable of multiplying two natural numbers with many digits in parallel. The proposed method employs the divide and conquer strategy (i.e., segmenting the numbers into units, tens, hundreds, thousands, etc.), to optimize the processing time of the arithmetic operations, and every two units are treated by a single neuron that can calculate up to 9 9 sequentially. The use of one neuron to perform a sequential multiplication represents the best improvement in terms of the number of neurons that has been reported to date. |
| Databáze: | OpenAIRE |
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