Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Ali Mirzaeian"'
Publikováno v:
IEEE Access, Vol 9, Pp 92881-92900 (2021)
This paper presents AVATAR, a learning-assisted Trojan testing flow to detect hardware Trojans placed into fabricated ICs at an untrusted foundry, without needing a Golden IC. AVATAR is a side-channel delay-based testing solution that is assisted by
Externí odkaz:
https://doaj.org/article/50d4411a0fa9418da235934a24af0ef9
Autor:
Tanmoy Chowdhury, Ashkan Vakil, Banafsheh Saber Latibari, Seyed Aresh Beheshti Shirazi, Ali Mirzaeian, Xiaojie Guo, Sai Manoj P D, Houman Homayoun, Ioannis Savidis, Liang Zhao, Avesta Sasan
Publikováno v:
Proceedings of the Great Lakes Symposium on VLSI 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c081c84512dc5c0e29c3451cc65280b9
https://doi.org/10.1145/3526241.3530831
https://doi.org/10.1145/3526241.3530831
Publikováno v:
ISQED
This paper proposes an ensemble learning model that is resistant to adversarial attacks. To build resilience, we introduced a training process where each member learns a radically distinct latent space. Member models are added one at a time to the en
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0687101fdd1030b6988ae37b07968360
http://arxiv.org/abs/2006.15127
http://arxiv.org/abs/2006.15127
Publikováno v:
ISQED
In this paper, we introduce a Learning Assisted Side Channel delay Analysis (LASCA) methodology for Hardware Trojan detection. Our proposed solution, unlike the prior art, does not require a Golden IC. Instead, it trains a Neural Network to act as a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f252befeeb9a9c5329c33c5704da00de
https://doi.org/10.1109/isqed48828.2020.9137007
https://doi.org/10.1109/isqed48828.2020.9137007
Autor:
Farnaz Behnia, Khaled N. Khasawneh, Liang Zhao, Mohammad Sabokrou, Tinoosh Mohsenin, Ali Mirzaeian, Avesta Sasan, Houman Homayoun, Sai Manoj
Publikováno v:
ISQED
In this paper, we propose Code-Bridged Classifier (CBC), a framework for making a Convolutional Neural Network (CNNs) robust against adversarial attacks without increasing or even by decreasing the overall models' computational complexity. More speci
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::16aee2c7c20a9297beb5863acc1d7517
http://arxiv.org/abs/2001.06099
http://arxiv.org/abs/2001.06099
Publikováno v:
ASP-DAC
With the outsourcing of design flow, ensuring the security and trustworthiness of integrated circuits has become more challenging. Among the security threats, IC counterfeiting and recycled ICs have received a lot of attention due to their inferior q
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f5b4f59d4b39a36fa3b52d9d257b9c4
Publikováno v:
ASP-DAC
In this paper, we present NESTA, a specialized Neural engine that significantly accelerates the computation of convolution layers in a deep convolutional neural network, while reducing the computational energy. NESTA reformats Convolutions into 3 ×
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::281c9af7a9e03b265964af679112124a
https://doi.org/10.1109/asp-dac47756.2020.9045135
https://doi.org/10.1109/asp-dac47756.2020.9045135
Publikováno v:
ISQED
Deep convolutional neural networks have shown high efficiency in computer visions and other applications. However, with the increase in the depth of the networks, the computational complexity is growing exponentially. In this paper, we propose a nove
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3fe980515df496ee462694ce8e3737b2
Publikováno v:
ReConFig
In this paper, we first propose the design of Temporal-Carry-deferring MAC (TCD-MAC) and illustrate how our proposed solution can gain significant energy and performance benefit when utilized to process a stream of input data. We then propose using t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4faa484b15faefb15ca956ac029bfc1c