A systolic array exploiting the inherent parallelisms of artificial neural networks
| Autor: | Jai-Hoon Chung, Seungryoul Maeng, Hyunsoo Yoon |
|---|---|
| Rok vydání: | 1992 |
| Předmět: |
Very-large-scale integration
Quantitative Biology::Neurons and Cognition Artificial neural network Computer science Time delay neural network Pipeline (computing) Computer Science::Neural and Evolutionary Computation General Engineering Systolic array Parallel computing Backpropagation Factor (programming language) Parallelism (grammar) Multiplication computer computer.programming_language |
| Zdroj: | ICPP (1) |
| ISSN: | 0165-6074 |
| DOI: | 10.1016/0165-6074(92)90017-2 |
| Popis: | The systolic array implementation of artificial neural networks is one of the best solutions to the communication problems generated by the highly interconnected neurons. In this paper, a two-dimensional systolic array for backpropagation neural network is presented. The design is based on the classical systolic algorithm of matrix-by-vector multiplication, and exploits the inherent parallelisms of backpropagation neural networks. This design executes the forward and backward passes in parallel, and exploits the pipelined parallelism of multiple patterns in each pass. The estimated performance of this design shows that the pipelining of multiple patterns is an important factor in VLSI neural network implementations. |
| Databáze: | OpenAIRE |
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