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pro vyhledávání: '"Varshika, M. Lakshmi"'
We propose a design methodology to facilitate fault tolerance of deep learning models. First, we implement a many-core fault-tolerant neuromorphic hardware design, where neuron and synapse circuitries in each neuromorphic core are enclosed with astro
Externí odkaz:
http://arxiv.org/abs/2204.02942
Autor:
Huynh, Phu Khanh, Varshika, M. Lakshmi, Paul, Ankita, Isik, Murat, Balaji, Adarsha, Das, Anup
Recently, both industry and academia have proposed several different neuromorphic systems to execute machine learning applications that are designed using Spiking Neural Networks (SNNs). With the growing complexity on design and technology fronts, pr
Externí odkaz:
http://arxiv.org/abs/2202.08897
Autor:
Varshika, M. Lakshmi, Balaji, Adarsha, Corradi, Federico, Das, Anup, Stuijt, Jan, Catthoor, Francky
As spiking-based deep learning inference applications are increasing in embedded systems, these systems tend to integrate neuromorphic accelerators such as $\mu$Brain to improve energy efficiency. We propose a $\mu$Brain-based scalable many-core neur
Externí odkaz:
http://arxiv.org/abs/2111.11838
The design of many-core neuromorphic hardware is getting more and more complex as these systems are expected to execute large machine learning models. To deal with the design complexity, a predictable design flow is needed to guarantee real-time perf
Externí odkaz:
http://arxiv.org/abs/2108.12444
Akademický článek
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Autor:
Varshika, M. Lakshmi, Balaji, Adarsha, Corradi, Federico, Das, Anup, Stuijt, Jan, Catthoor, Francky
As spiking-based deep learning inference applications are increasing in embedded systems, these systems tend to integrate neuromorphic accelerators such as $��$Brain to improve energy efficiency. We propose a $��$Brain-based scalable many-cor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::896d41f7c038c77ca3de6f38f502fede