An On-Line Arithmetic-Based Recon gurable Neuroprocessor

Arti cial neural networks can solve complex problems such as time series prediction, handwritten pattern recognition or speech processing. Though software simulations are essential when one sets about to study a new algorithm, they cannot always ful ll real-time criteria required by some practical applications. Consequently, hardware implementations are of crucial import. The appearance of fast recon gurable FPGA circuits brings about new paths for the design of neuroprocessors. All arithmetic operations are carried out with on-line operators. This short paper brie y describes recon gurable FPGA-based neural networks and gives an introduction to on-line arithmetic.