Implementation of Deep Neural Network Using VLSI by Integral Stochastic Computation

• Published in: Journal of physics. Conference series 2021-07, Vol.1964 (6), p.62091
• Main Authors: Khan, Vijitha, Parameshwaran, R, Arulkumaran, G, Gopi, B
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Computing; Deep neural network; Energy costs; FPGA; Genetic algorithms; Information systems; Integrated circuits; Machine learning; Neural networks; VLSI
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/1964/6/062091

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.