Gilberto Contreras - Dept. of Electrical and Computer Engineering, The University of Texas at El Paso
Patricia Nava
Faridoon Shabani

Specialized hardware implementations of Artificial Neural Networks (ANNs) can offer faster execution than general-purpose microprocessors by taking advantage of reusable modules, parallel processes and specialized computational components. Modern high-density FPGAs offer the required flexibility and fast design-to-implementation time with the possibility of exploitinghighly parallel computations like those required by ANNs in hardware. This paper presents a fast digital ANN implemented in a Xilinx FPGA Spartan II device with performance of 150x106 connections per second (CPS

Neural Networks, ANN, VHDL, FPGA, Digital Neuro-Processor