Open AccessImplementation of Deep Neural Network Using VLSI by Integral Stochastic Computation
Author(s) -
Vijitha Khan,
R. Parameshwaran,
G. Arulkumaran,
B. Gopi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1964/6/062091
Subject(s) - computer science , reinforcement learning , stochastic computing , artificial intelligence , artificial neural network , machine learning , computation , computational learning theory , reliability (semiconductor) , inference , pace , computer engineering , theoretical computer science , power (physics) , active learning (machine learning) , algorithm , physics , geodesy , quantum mechanics , geography
Efficient machine learning techniques that need substantial equipment and power usage in its computation phase are computational models. Stochastic computation has indeed been added and the solution a compromise between this ability of the project and information systems and organisations to introduce computational models. Technical specifications and energy cost are greatly diminished in Stochastic Computing by marginally compromising the precision of inference and calculation pace. However, Sc Neural Network models’ efficiency has also been greatly enhanced with recent advances in SC technologies, making it equivalent to standard relational structures and fewer equipment types. Developers start with both the layout of a rudimentary SC nerve cell throughout this essay and instead study different kinds of SC machine learning, including word embedding, reinforcement learning, convolutionary genetic algorithms, and reinforcement learning. Consequently, rapid developments in SC architectures that further enhance machine learning’s device speed and reliability are addressed. Both for practice and prediction methods, the generalised statement and simplicity of SC Machine Learning are demonstrated. After this, concerning conditional alternatives, the strengths and drawbacks of SC Machine learning are addressed.