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book article EST / book article ENG

AdAM: adaptive fault-tolerant approximate multiplier for edge DNN acceleratorsTaheri, Mahdi; Cherezova, Natalia; Nazari, Samira; Rafiq, Ahsan; Azarpeyvand, Ali; Ghasempouri, Tara; Daneshtalab, Masoud; Raik, Jaan; Jenihhin, Maksim2024 IEEE European Test Symposium (ETS): ETS 2024 : May 20-24, 2024, The Hague, Netherlands : proceedings2024 https://doi.org/10.1109/ETS61313.2024.10567161 Conference Proceedings at Scopus Article at Scopus Article at WOS

book article EST / book article ENG
2

book article

Challenges in using neural networks in safety-critical applicationsForsberg, H.; Linden, Jan; Hjorth, J.; Manefjord, T.; Daneshtalab, MasoudAIAA/IEEE Digital Avionics Systems Conference - Proceedings, 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), Virtual Conference, October 11-16, 2020 : Proceedings2020 / 7 p https://doi.org/10.1109/DASC50938.2020.9256519

book article
3

book article

Composing graph theory and deep neural networks to evaluate SEU type soft error effectsBalakrishnan, Aneesh; Lange, Thomas; Glorieux, Maximilien; Alexandrescu, Dan; Jenihhin, Maksim9th Mediterranean Conference on Embedded Computing (MECO'2020), Budva, Montenegro, 8-11 June 20202020 https://doi.org/10.1109/MECO49872.2020.9134279

book article
Related publications 1
- A synthetic, hierarchical approach for modelling and managing complex systems' quality and reliability = Sünteetiline, hierarhiline lähenemine keerukate süsteemide kvaliteedi ja töökindluse modelleerimiseks ja haldamiseks
4

book article EST / book article ENG

Data augmentation and teacher-student training for LF-MMI based robust speech recognitionUllah, Asad; Alumäe, TanelText, Speech, and Dialogue : 21st International Conference, TSD 2018, Brno, Czech Republic, September 11-14, 2018 : proceedings2018 / p. 403-410 https://doi.org/10.1007/978-3-030-00794-2_43 Conference Proceedings at Scopus Article at Scopus Article at WOS

book article EST / book article ENG
5

journal article

DeepAxe : a framework for exploration of approximation and reliability trade-offs in DNN acceleratorsTaheri, Mahdi; Riazati, Mohamad; Ahmadilivani, Mohammad Hasan; Jenihhin, Maksim; Daneshtalab, Masoud; Raik, Jaan; Sjödin, Mikael; Lisper, BjörnarXiv.org2023 / 8 p. : ill https://doi.org/10.48550/arXiv.2303.08226

journal article
6

book article

DeepHLS: A complete toolchain for automatic synthesis of deep neural networks to FPGARiazati, Mohammad; Daneshtalab, Masoud; Sjodin, Mikael; Lisper, BjornICECS 2020 - 27th IEEE International Conference on Electronics, Circuits and Systems, November 23-25, 2020, Virtual Conference : Proceedings2020 / 4 p https://doi.org/10.1109/ICECS49266.2020.9294881

book article
7

book article EST / book article ENG

FARMUR: fair adversarial retraining to mitigate unfairness in robustnessMousavi, Seyed Ali; Mousavi, Hamid; Daneshtalab, MasoudAdvances in Databases and Information Systems: 27th European Conference, ADBIS 2023, Barcelona, Spain, September 4–7, 2023 : proceedings2023 / p. 133-145 https://doi.org/10.1007/978-3-031-42914-9_10 Conference proceedings at Scopus Article at Scopus

book article EST / book article ENG
8

book article

Gate-level graph representation learning : a step towards the improved stuck-at faults analysisBalakrishnan, Aneesh; Alexandrescu, Dan; Jenihhin, Maksim; Lange, Thomas; Glorieux, MaximilienProceedings of the Twenty Second International Symposium on Quality Electronic Design (ISQED) : Santa Clara, USA, 7-9 April 20212021 / p. 24-30 https://doi.org/10.1109/ISQED51717.2021.9424256

book article
Related publications 1
- A synthetic, hierarchical approach for modelling and managing complex systems' quality and reliability = Sünteetiline, hierarhiline lähenemine keerukate süsteemide kvaliteedi ja töökindluse modelleerimiseks ja haldamiseks
9

book article EST / book article ENG

Special session : approximation and fault resiliency of DNN acceleratorsAhmadilivani, Mohammad Hasan; Barbareschi, Mario; Barone, Salvatore; Bosio, Alberto; Daneshtalab, Masoud; Torca, Salvatore Della; Gavarini, Gabriele; Jenihhin, Maksim; Raik, Jaan; Taheri, MahdiProceedings 2023 IEEE 41st VLSI Test Symposium (VTS)2023 / 10 p. : ill https://doi.org/10.1109/VTS56346.2023.10140043 Conference proceeding at Scopus Article at Scopus Article at WOS

book article EST / book article ENG
10

book article EST / book article ENG

Special session: reliability assessment recipes for DNN acceleratorsAhmadilivani, Mohammad Hasan; Bosio, Alberto; Deveautour, Bastien; Dos Santos, Fernando Fernandes; Guerrero-Balaguera, Juan-David; Jenihhin, Maksim; Kritikakou, Angeliki; Sierra, Robert Limas; Raik, Jaan; Taheri, Mahdi42nd IEEE VLSI Test Symposium, VTS 20242024 / 11 p. : ill https://doi.org/10.1109/VTS60656.2024.10538707 Conference proceedings at Scopus Article at Scopus Article at WOS

book article EST / book article ENG
11

journal article EST / journal article ENG

A systematic literature review on hardware reliability assessment methods for deep neural networksAhmadilivani, Mohammad Hasan; Taheri, Mahdi; Raik, Jaan; Daneshtalab, Masoud; Jenihhin, MaksimACM Computing Surveys2024 / art. 141 https://doi.org/10.1145/3638242 Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

journal article EST / journal article ENG
12

book article EST / book article ENG

ZED-TTE: Zone embedding and deep neural network based travel time estimation approachOunoughi, Chahinez; Yeferny, Taoufik; Ben Yahia, Sadok2021 International Joint Conference on Neural Networks (IJCNN) : proceedings2021 / 10 p https://doi.org/10.1109/IJCNN52387.2021.9533456 Conference Proceedings at Scopus Article at Scopus Article at WOS

book article EST / book article ENG

Number of records 12, displaying 1 - 12

1. deep neural networks
2. deep neural networks (DNNs)
3. deep convolutional neural network
4. deep neural network
5. deep neural network compression
6. artificial neural networks
7. artificial neural networks (ANN)
8. constrained artificial neural networks
9. Constrained neural networks
10. convolutional neural networks
11. convolutional neural networks (CNNa)
12. Convolutional Neural Networks (CNNs)
13. Fuzzy neural networks
14. graph neural networks
15. multilayer neural networks
16. neural networks
17. pre-trained neural networks
18. recurrent neural networks
19. recurrent neural networks (RNN)
20. deep donor-deep acceptor pairs
21. deep brain stimulation
22. Deep defects
23. deep energy levels
24. deep energy renovation
25. deep eutectic solvents
26. deep fake
27. deep fake recognition
28. deep habits
29. deep heat well
30. deep inference
31. deep layer
32. deep learning
33. deep learning (DL)
34. deep learning classifier
35. deep learning method
36. deep learning methods
37. deep learning model
38. deep learning model quantization
39. deep learning network
40. deep level
41. deep level traps
42. deep levels
43. deep offshore
44. deep packet inspection
45. deep reinforcement learning
46. Deep reinforcing learning SDN
47. deep renovation
48. deep understanding
49. Deep-learning
50. deep-learning (DL)
51. embedded deep learning
52. natural deep eutectic solvents
53. nZEB deep energy renovation
54. nZEB deep renovation
55. artificial neural network
56. Binarized Neural Network (BNN)
57. cascaded forward neural network (CFNN)
58. Convolutional Neural Network
59. convolutional neural network (CNN)
60. feedforward neural network (FFNN)
61. Graph Convolutional Neural Network (GCN)
62. GraphSAGE (Graph Based Neural Network)
63. human neural progenitor cells
64. linear Delta robots neural network based model
65. neural activity
66. neural additive model
67. neural architecture search
68. neural controller
69. neural development
70. neural fuzzy modelling control
71. neural machine translation
72. neural network
73. neural network architecture search
74. neural network controller
75. neural network modelling
76. neural network predictive controller
77. neural oscillations
78. neural processing unit
79. neural stimulation
80. Radial Basis Function Neural Network (RBFNN)
81. recurrent neural network language model
82. spiking neural network (SNN)
83. Ternary Neural Network
84. adaptive networks
85. ad-hoc sensor networks security
86. automotive communication networks
87. Bayesian Belief Networks
88. Bayesian networks
89. binary search networks
90. binary sensor networks
91. body area networks
92. body sensor networks
93. cellular networks
94. communication networks
95. complex networks
96. computer networks
97. content distribution networks
98. content-addressable storage networks
99. convolutional networks
100. cross-border communication networks
101. decentralized content-addressable storage (DCAS) networks
102. developmental networks
103. diffusion networks
104. digital twin networks (DTNs)
105. distribution networks
106. district heating networks
107. Dynamic networks
108. dynamics over networks
109. ecological networks
110. economic networks
111. electric power transmission networks
112. electrical distribution networks
113. electrical networks
114. embedded networks/NoC
115. gateways (computer networks)
116. generative adversarial networks
117. geodetic networks
118. global production networks
119. graph attention networks
120. graph convolutional networks
121. heating and cooling networks
122. heating networks
123. hypar-formed networks
124. IaaS cloud networks
125. Impedance-source networks
126. innovation networks
127. IP networks
128. iterative networks
129. knowledge networks
130. location-based social networks
131. logistics networks
132. Long Short-Term Memory Networks (LSTMs)
133. low voltage networks
134. low-voltage networks
135. lumped parameter networks
136. marketing networks
137. metabolic networks and pathways
138. mobile actuator-sensor networks (MAS-Net)
139. multilayer networks
140. NB-IoT networks
141. networks
142. networks coding
143. neuro-fuzzy networks and guided missile
144. non-terrestrial networks
145. observational networks
146. on-body networks (BANs)
147. Online social networks
148. Optical fiber networks
149. optical fibre networks
150. optical networks
151. optimized routing approach for critical and emergency networks
152. optimized routing approach for critical and emergency networks simulations
153. packet switched networks
154. partner networks
155. Passive networks
156. phantom cellular networks
157. private networks
158. production networks
159. Public safety networks
160. public safety networks (PSN)
161. public safety networks (PSNs)
162. reconfigurable scan networks
163. regional innovation networks
164. scale-free networks
165. sensor networks
166. small district heating networks
167. SME networks
168. social networks
169. social networks
170. software defined networks (SDN)
171. sorting networks
172. supply networks
173. transmission networks
174. unreliable wireless networks
175. WBANs (wireless body area networks)
176. wearable wireless networks
177. wearable wireless networks (WWNs)
178. weighted networks
179. wireless ad hoc sensor networks
180. Wireless Body Area Networks
181. wireless body area networks (WBANs)
182. wireless body-to-body networks (BBN)
183. wireless networks
184. wireless sensor networks
185. virtual networks
186. 5G networks
187. 5G networks

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