Title :
An embedded global addressing technique for scalable neural architectures
Author :
Alhalabi, Bassem ; Bayoumi, Magdy
Author_Institution :
Dept. of Comput. Sci. & Eng., Florida Atlantic Univ., Boca Raton, FL, USA
Abstract :
This paper proposes an embedded addressing technique suitable for chip-set based scalable neural network systems. A detailed hardware design and the corresponding algorithms are developed. The novel feature of the developed technique is that it effectively eliminates the need for all off-chip address decoding hardware, making the overall system self-contained. Moreover, this technique makes hardware scalability easier to implement than other standard addressing schemes.
Keywords :
feedforward neural nets; integrated circuit design; neural chips; chip-set based neural network systems; embedded global addressing technique; hardware design; hardware scalability; scalable neural architectures; self-contained system; Bandwidth; Computer architecture; Computer science; Decoding; Feedforward systems; Hardware; Neural networks; Neurons; Scalability; Testing;
Conference_Titel :
Circuits and Systems, 1997. Proceedings of the 40th Midwest Symposium on
Print_ISBN :
0-7803-3694-1
DOI :
10.1109/MWSCAS.1997.662311