Mixing floating- and fixed-point formats for neural network learning on neuroprocessors
We examine the efficient implementation of back-propagation (BP) type algorithms on TO [3], a vector processor with a fixed-point engine, designed for neural network simulation. Using Matrix Back Propagation (MBP) [2]we achieve an asymptotically optimal performance on TO (about 0.8 GOPS) for both fo...
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| Published in | Microprocessing and microprogramming Vol. 41; no. 10; pp. 757 - 769 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.06.1996
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0165-6074 |
| DOI | 10.1016/0165-6074(96)00012-9 |
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| Abstract | We examine the efficient implementation of back-propagation (BP) type algorithms on TO [3], a vector processor with a fixed-point engine, designed for neural network simulation. Using Matrix Back Propagation (MBP) [2]we achieve an asymptotically optimal performance on TO (about 0.8 GOPS) for both forward and backward phases, which is not possible with the standard on-line BP algorithm. We use a mixture of fixed- and floating-point operations in order to guarantee both high efficiency and fast convergence. Though the most expensive computations are implemented in fixed-point, we achieve a rate of convergence that is comparable to the floating-point version. The time taken for conversion between fixed- and floating-point is also shown to be reasonably low. |
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| AbstractList | We examine the efficient implementation of back-propagation (BP) type algorithms on TO [3], a vector processor with a fixed-point engine, designed for neural network simulation. Using Matrix Back Propagation (MBP) [2]we achieve an asymptotically optimal performance on TO (about 0.8 GOPS) for both forward and backward phases, which is not possible with the standard on-line BP algorithm. We use a mixture of fixed- and floating-point operations in order to guarantee both high efficiency and fast convergence. Though the most expensive computations are implemented in fixed-point, we achieve a rate of convergence that is comparable to the floating-point version. The time taken for conversion between fixed- and floating-point is also shown to be reasonably low. We examine the efficient implementation of back-propagation (BP) type algorithms on T0 [3], a vector processor with a fixed-point engine, designed for neural network simulation. Using Matrix Back Propagation (MBP) [2] we achieve an asymptotically optimal performance on T0 (about 0.8 GOPS) for both forward and backward phases, which is not possible with the standard on-line BP algorithm. We use a mixture of fixed- and floating-point operations in order to guarantee both high efficiency and fast convergence. Though the most expensive computations are implemented in fixed-point, we achieve a rate of convergence that is comparable to the floating-point version. The time taken for conversion between fixed- and floating-point is also shown to be reasonably low. |
| Author | Anguita, Davide Gomes, Benedict A. |
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| CitedBy_id | crossref_primary_10_1016_S1383_7621_99_00017_X crossref_primary_10_1016_j_neunet_2014_09_003 crossref_primary_10_1016_j_neucom_2007_12_006 crossref_primary_10_1631_jzus_C1000137 |
| Cites_doi | 10.1109/72.143374 10.1007/BF00332914 10.1109/78.215327 10.1016/0893-6080(90)90006-7 10.1162/neco.1992.4.6.835 10.1109/72.129414 10.1162/neco.1990.2.3.363 10.1007/BF02551274 10.1002/aic.690370209 10.1142/S0129065793000250 10.1109/72.207614 10.1016/0925-2312(94)90034-5 10.1109/72.217180 10.1109/72.286885 |
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