Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/149972
Title: Temperature compensation for analog machine learners (II)
Authors: Lee, Shawn Wei Han
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Lee, S. W. H. (2021). Temperature compensation for analog machine learners (II). Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149972
Project: A2018-201
Abstract: The widespread adoption of the Internet of Things (IoT) in everyday life has increased demand for ever-increasing computational resources in cloud computing. The use of analogue processing and the extreme machine learning (ELM) algorithm in the design of ultra-low power machine learners for "smart" sensors has proven to be beneficial. However, due to sub-threshold transistor operation, the reliance of these systems' weights on temperature cannot be overlooked. The aim of this project is to use behavioral simulations to determine the best form of temperature behavior for current reference in this framework. State-of-the-art IC modeling software and CMOS processes will be used to design and simulate the corresponding circuits
URI: https://hdl.handle.net/10356/149972
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
Lee Wei Han Shawn's FYP report.pdf
  Restricted Access
721.95 kBAdobe PDFView/Open

Page view(s)

107
Updated on May 15, 2022

Download(s)

3
Updated on May 15, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.