Power Consumption Reduction in IoT Devices Through Field-Programmable Gate Array with Nanobridge Switch

Sharma, Preeti; Nair, Rajit; Dwivedi, Vijay Kumar

doi:10.1007/978-981-15-7130-5_54

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Given the same model size, data, and computing power, the contextual representations learnt this way beat those trained by BERT. On the GLUE natural language understanding benchmark, an algorithm trained on one GPU for four days outperforms the state-of-the-art GPT (learned using 30 times more CPU) [ 25 ]. Regarding performance at scale, the technique is comparable to RoBERT and XLNet while utilising less than a quarter of their computation and surpassing them when using the same amount of computing [ 26 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

Analysing Hate Speech against Migrants and Women through Tweets Using Ensembled Deep Learning Model

Hasan

Sharma²,

Khan

et al. 2022

Computational Intelligence and Neuroscience

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Twitter’s popularity has exploded in the previous few years, making it one of the most widely used social media sites. As a result of this development, the strategies described in this study are now more beneficial. Additionally, there has been an increase in the number of people who express their views in demeaning ways to others. As a result, hate speech has piqued interest in the subject of sentiment analysis, which has developed various algorithms for detecting emotions in social networks using intuitive means. This paper proposes the deep learning model to classify the sentiments in two separate analyses. In the first analysis, the tweets are classified based on the hate speech against the migrants and the women. In the second analysis, the detection is performed using a deep learning model to organise whether the hate speech is performed by a single or a group of users. During the text analysis, word embedding is implemented using the combination of deep learning models such as BiLSTM, CNN, and MLP. These models are integrated with word embedding methods such as inverse glove (global vector), document frequency (TF-IDF), and transformer-based embedding.

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Section: Proposed Methodologymentioning

confidence: 99%

Analysing Hate Speech against Migrants and Women through Tweets Using Ensembled Deep Learning Model

Hasan

Sharma²,

Khan

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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“…The stateful temporal logic algebra system is realizable as a neuromorphic circuit built with the seven building blocks FA , LA , D , C , M , I , R and is implementable for various hardware target architectures. It is especially suited for implementation in CMOS (Nair et al, 2020 ; Han et al, 2021 ), FPGA (Yang et al, 2021a ), and quantum-based hardware (Varadarajan, 2014 ; Gonzalez-Raya et al, 2019 ; Hamilton et al, 2019 ; Shi et al, 2019 ; Lamata, 2020 ; Marković et al, 2020 ) as nanobridge atomic switch FPGAs (Demis et al, 2015 ; Sharma et al, 2021 ) superconducting accelerators (Tzimpragos et al, 2020 ; Vakili et al, 2020 ; Feldhoff and Toepfer, 2021 ), superconducting nanowires (Toomey et al, 2019 ), nanowire networks (Diaz-Alvarez et al, 2020 ; Kendall et al, 2020 ; Kuncic et al, 2020 ; Li et al, 2020 ; Milano et al, 2020 ; Dunham et al, 2021 ; Kendall, 2021 ) and memristors (Sanz et al, 2018 ; Woźniak et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Periodicity Pitch Perception Part III: Sensibility and Pachinko Volatility

et al. 2022

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Neuromorphic computer models are used to explain sensory perceptions. Auditory models generate cochleagrams, which resemble the spike distributions in the auditory nerve. Neuron ensembles along the auditory pathway transform sensory inputs step by step and at the end pitch is represented in auditory categorical spaces. In two previous articles in the series on periodicity pitch perception an extended auditory model had been successfully used for explaining periodicity pitch proved for various musical instrument generated tones and sung vowels. In this third part in the series the focus is on octopus cells as they are central sensitivity elements in auditory cognition processes. A powerful numerical model had been devised, in which auditory nerve fibers (ANFs) spike events are the inputs, triggering the impulse responses of the octopus cells. Efficient algorithms are developed and demonstrated to explain the behavior of octopus cells with a focus on a simple event-based hardware implementation of a layer of octopus neurons. The main finding is, that an octopus' cell model in a local receptive field fine-tunes to a specific trajectory by a spike-timing-dependent plasticity (STDP) learning rule with synaptic pre-activation and the dendritic back-propagating signal as post condition. Successful learning explains away the teacher and there is thus no need for a temporally precise control of plasticity that distinguishes between learning and retrieval phases. Pitch learning is cascaded: At first octopus cells respond individually by self-adjustment to specific trajectories in their local receptive fields, then unions of octopus cells are collectively learned for pitch discrimination. Pitch estimation by inter-spike intervals is shown exemplary using two input scenarios: a simple sinus tone and a sung vowel. The model evaluation indicates an improvement in pitch estimation on a fixed time-scale.

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