An expandable hardware platform for implementation of CNN-based applications
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
| Hi-index | 0.00 |
The paper addresses the issue of implementing an embedded global analogic programming unit (GAPU) on the reconfigurable emulated-digital cellular neural-nonlinear networks universal machine (CNN-UM) architecture that has been extended by a flexible Xilinx MicroBlaze soft processor core to take full advantage of the joint computing power of high-speed distributed arithmetics and programmability. The implemented GAPU provides a stand-alone operation, which is capable of controlling complex sophisticated CNN analogic algorithms similar to various visual microprocessors, such as the ACE4k, ACE16k, and Bi-i vision systems. The quality of the embedded GAPU implementation is demonstrated by an analogic algorithm, in which sequences of template operations are required. Based on the experiments, several important issues relating to the acceleration efficiency, accuracy, cell size, and area consumption are discussed and compared with different CNN-UM implementations. Copyright © 2008 John Wiley & Sons, Ltd.