Zobrazeno 1 - 10
of 430
pro vyhledávání: '"Bagherzadeh, Nader"'
The Convolutional Neural Network (CNN) has emerged as a powerful and versatile tool for artificial intelligence (AI) applications. Conventional computing architectures face challenges in meeting the demanding processing requirements of compute-intens
Externí odkaz:
http://arxiv.org/abs/2402.04431
Stock trend analysis has been an influential time-series prediction topic due to its lucrative and inherently chaotic nature. Many models looking to accurately predict the trend of stocks have been based on Recurrent Neural Networks (RNNs). However,
Externí odkaz:
http://arxiv.org/abs/2305.14368
Autor:
Nabiee, Shima, Bagherzadeh, Nader
Market financial forecasting is a trending area in deep learning. Deep learning models are capable of tackling the classic challenges in stock market data, such as its extremely complicated dynamics as well as long-term temporal correlation. To captu
Externí odkaz:
http://arxiv.org/abs/2303.09323
This paper proposes a novel two-stage framework for emotion recognition using EEG data that outperforms state-of-the-art models while keeping the model size small and computationally efficient. The framework consists of two stages; the first stage in
Externí odkaz:
http://arxiv.org/abs/2208.00883
Autor:
Murillo, Raul, Del Barrio, Alberto A., Botella, Guillermo, Kim, Min Soo, Kim, HyunJin, Bagherzadeh, Nader
The Posit Number System was introduced in 2017 as a replacement for floating-point numbers. Since then, the community has explored its application in Neural Network related tasks and produced some unit designs which are still far from being competiti
Externí odkaz:
http://arxiv.org/abs/2102.09262
This paper analyzes the effects of approximate multiplication when performing inferences on deep convolutional neural networks (CNNs). The approximate multiplication can reduce the cost of the underlying circuits so that CNN inferences can be perform
Externí odkaz:
http://arxiv.org/abs/2007.10500
We propose a lightweight scheme where the formation of a data block is changed in such a way that it can tolerate soft errors significantly better than the baseline. The key insight behind our work is that CNN weights are normalized between -1 and 1
Externí odkaz:
http://arxiv.org/abs/2001.08806
Publikováno v:
In Sustainable Computing: Informatics and Systems December 2023 40
Akademický článek
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Autor:
Montoya, Eric Arturo, Chen, Jen-Ru, Ngelale, Randy, Lee, Han Kyu, Tseng, Hsin-Wei, Wan, Lei, Yang, En, Braganca, Patrick, Boyraz, Ozdal, Bagherzadeh, Nader, Nilsson, Mikael, Krivorotov, Ilya N.
Spin transfer torque magnetic random access memory (STT-MRAM) is a promising candidate for next generation memory as it is non-volatile, fast, and has unlimited endurance. Another important aspect of STT-MRAM is that its core component, the nanoscale
Externí odkaz:
http://arxiv.org/abs/1909.11360