Temperature compensation for analog machine learners (II) - Lee, Shawn Wei Han (Nanyang Technological University, 2021)
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