Machine Intelligence Techniques in VLSI Hardware
Ashish Chauhan1, Laxmi Singh2, Sanjeev Kumar Gupta3
1Dr. Ashish Chauhan, Department of Electronics & Communication Engineering, Rabindranath Tagore University, Bhopal (Madhya Pradesh), India.
2Dr. Laxmi Singh, Department of Electronics & Communication Engineering, Rabindranath Tagore University, Bhopal (Madhya Pradesh), India.
3Dr. Sanjeev Kumar Gupta, Department of Electronics & Communication Engineering, Rabindranath Tagore University, Bhopal (Madhya Pradesh), India.
Manuscript received on 30 September 2019 | Revised Manuscript received on 12 November 2019 | Manuscript Published on 22 November 2019 | PP: 1623-1626 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F13020986S319/19©BEIESP | DOI: 10.35940/ijeat.F1302.0986S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A brief guide to storage primarily dependent strategies to simulate machine intelligence in VLSI equipment, explaining the difficulties accompanied by the advantages. The application of programming methods in VLSI hardware can be both difficult and reasonable. Deep architectures, stratified temporal reminiscences and memory networks square measure a number of up-to-date approaches in this analysis area. This methodology was built to follow the low-level intelligence challenges and seeks to provide climbable approaches to high-level intelligence problems such as thin writing and debat
Keywords: Component, Formatting, Style, Styling, Insert (keywords).
Scope of the Article: Computer Architecture and VLSI