Name
Abstract | ||
|---|---|---|
In this paper emerging parallel/distributed architectures are explored for the digital VLSI implementation of adaptive bidirectional associative memory (BAM) neural network. A single instruction stream many data stream (SIMD)-based parallel processing architecture, is developed for the adaptive BAM neural network, taking advantage of the inherent parallelism in BAM. This novel neural processor architecture is named the sliding feeder BAM array processor (SLiFBAM). The SLiFBAM processor can be viewed as a two-stroke neural processing engine, It has four operating modes: learn pattern, evaluate pattern, read weight, and write weight. Design of a SLiFBAM VLSI processor chip is also described. By using 2-μm scalable CMOS technology, a SLiFBAM processor chip with 4+4 neurons and eight modules of 256×5 bit local weight-storage SRAM, was integrated on a 6.9×7.4 mm2 prototype die. The system architecture is highly flexible and modular, enabling the construction of larger BAM networks of up to 252 neurons using multiple SLiFBAM chips |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1109/72.557697 | IEEE Transactions on Neural Networks |
Field | DocType | Volume |
Content-addressable memory,Bidirectional associative memory,Computer science,SIMD,Artificial intelligence,Systems architecture,Computer hardware,Very-large-scale integration,Microarchitecture,Pattern recognition,Chip,Vector processor,Embedded system | Journal | 8 |
Issue | ISSN | Citations |
2 | 1045-9227 | 10 |
PageRank | References | Authors |
0.74 | 22 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. M.R. Hasan | 1 | 10 | 0.74 |
| Ng Kang Siong | 2 | 10 | 0.74 |