An analog artificial neural network (ANN) classifier using a common-source amplifier based nonlinear activation function is presented in this work. A shallow ANN is designed in 65nm CMOS to perform binary classification on breast cancer dataset and identify each patient data as either benign or malignant. Use of common-source amplifier structure simplifies the ANN and results in only 39fJ/classification at 0.8V power supply and core area of only 240μm2. The classifier is trained using Matlab and validated using Spectre simulations.
|Title of host publication
|2019 IEEE 62nd International Midwest Symposium on Circuits and Systems, MWSCAS 2019
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - Aug 2019
|62nd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2019 - Dallas, United States
Duration: Aug 4 2019 → Aug 7 2019
|Midwest Symposium on Circuits and Systems
|62nd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2019
|8/4/19 → 8/7/19
- analog AI circuit
- artificial neural network
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Electrical and Electronic Engineering