Thesis Open Access
Eyob Gedlie
{ "DOI": "10.20372/nadre:5730", "author": [ { "family": "Eyob Gedlie" } ], "issued": { "date-parts": [ [ 2018, 12, 31 ] ] }, "abstract": "<p>The implementation of neural systems is presented in this paper. The thesis focuses on<br>\nimplementations where the algorithms and their physical support are tightly coupled. This thesis<br>\ndescribes a neural network intelligent, application, soft-algorithm to implement to hardware<br>\nelectronics device. With the emerging of Integrated Circuit, any design with large number of<br>\nelectronic components can be squeezed into a tiny chip area with minimum power requirements,<br>\nwhich leads to integration of innumerable applications so as to design any electronic consumer<br>\nproduct initiated in the era of digital convergence. One has many choices for selecting either of<br>\nthese reconfigurable techniques based on Speed, Gate Density, Development, Prototyping,<br>\nsimulation time and cost. This thesis describes the implementation in hardware of an Artificial<br>\nNeural Network with an Electronic circuit made up of Op-amps. The implementation of a Neural<br>\nNetwork in hardware can be desired to benefit from its distributed processing capacity or to avoid<br>\nusing a personal computer attached to each implementation. The hardware implementation is based<br>\nin a Feed Forward Neural Network, with a hyperbolic tangent as activation function, with floating<br>\npoint notation of single precision. The device used was an electronic circuit made with Op-amps<br>\nThe Proteus Software version 8.0 was used to validate the implementation results of the hardware<br>\ncircuit. The results show that the implementation does not introduce a noticeable loss of precision<br>\nbut is slower than the software implementation running in a PC.</p>", "title": "Investigation of Soft Neural Network Algorithm Implement to Analog Electronics Devices", "type": "thesis", "id": "5730" }
All versions | This version | |
---|---|---|
Views | 0 | 0 |
Downloads | 0 | 0 |
Data volume | 0 Bytes | 0 Bytes |
Unique views | 0 | 0 |
Unique downloads | 0 | 0 |