Abdolzadeh, Vida (2020) Efficient Implementation of Recurrent Neural Network Accelerators. [Tesi di dottorato]
Preview |
Text
Abdolzadeh_Vida_32.pdf Download (2MB) | Preview |
| Item Type: | Tesi di dottorato |
|---|---|
| Resource language: | English |
| Title: | Efficient Implementation of Recurrent Neural Network Accelerators |
| Creators: | Creators Email Abdolzadeh, Vida vida.abdolzadeh@unina.it |
| Date: | 13 March 2020 |
| Number of Pages: | 85 |
| Institution: | Università degli Studi di Napoli Federico II |
| Department: | Ingegneria Elettrica e delle Tecnologie dell'Informazione |
| Dottorato: | Information technology and electrical engineering |
| Ciclo di dottorato: | 32 |
| Coordinatore del Corso di dottorato: | nome email Riccio, Daniele daniele.riccio@unina.it |
| Tutor: | nome email Petra, Nicola UNSPECIFIED |
| Date: | 13 March 2020 |
| Number of Pages: | 85 |
| Keywords: | Long Short-Term Memory layer : LSTM Recurrent Neural Networks: RNN |
| Settori scientifico-disciplinari del MIUR: | Area 09 - Ingegneria industriale e dell'informazione > ING-INF/01 - Elettronica |
| Date Deposited: | 05 Apr 2020 15:17 |
| Last Modified: | 03 Sep 2026 09:34 |
| URI: | https://www.fedoa.unina.it/id/eprint/13225 |
Collection description
In this dissertation, we propose an accelerator for the implementation of Lthe ong Short-Term Memory layer in Recurrent Neural Networks. We analyze the effect of quantization on the accuracy of the network and we derive an architecture that improves the throughput and latency of the accelerator. The proposed technique only requires one training process, hence reducing the design time. We present the implementation results of the proposed accelerator. The performance compares favorably with other solutions presented in Literature. The goal of this thesis is to choose which circuit is better in terms of precision, area and timing. In addition, to verify that the chosen circuit works perfectly as activation functions, it is converted in Vivado HLS using C and then integrated in an LSTM Layer. A Speech recognition application has been used to test the system. The results are compared with the ones computed using the same Layer in Matlab to obtain the accuracy and to decide if the precision of the Non-Linear functions is sufficient.
Downloads
Downloads per month over past year
Actions (login required)
 |
View Item |
