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   Ray, Subhajit ; Kinget, Peter R. ( April 2023 , IEEE Journal of Solid-State Circuits)
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3. Modeling and Simulation of Circuit-Level Nonidealities for an Analog Computing Design Approach with Application to EEG Feature Extraction

   https://doi.org/10.1109/TCAD.2022.3170248  

   Mirchandani, Nikita ; Zhang, Yuqing ; Abdelfattah, Safaa ; Onabajo, Marvin ; Shrivastava, Aatmesh ( April 2022 , IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems)

   This paper presents a design approach for the modeling and simulation of ultra-low power (ULP) analog computing machine learning (ML) circuits for seizure detection using EEG signals in wearable health monitoring applications. In this paper, we describe a new analog system modeling and simulation technique to associate power consumption, noise, linearity, and other critical performance parameters of analog circuits with the classification accuracy of a given ML network, which allows to realize a power and performance optimized analog ML hardware implementation based on diverse application-specific needs. We carried out circuit simulations to obtain non-idealities, which are then mathematically modeled for an accurate mapping. We have modeled noise, non-linearity, resolution, and process variations such that the model can accurately obtain the classification accuracy of the analog computing based seizure detection system. Noise has been modeled as an input-referred white noise that can be directly added at the input. Device process and temperature variations were modeled as random fluctuations in circuit parameters such as gain and cut-off frequency. Nonlinearity was mathematically modeled as a power series. The combined system level model was then simulated for classification accuracy assessments. The design approach helps to optimize power and area during the development of tailored analog circuits for ML networks with the ability to potentially trade power and performance goals while still ensuring the required classification accuracy. The simulation technique also enables to determine target specifications for each circuit block in the analog computing hardware. This is achieved by developing the ML hardware model, and investigating the effect of circuit nonidealities on classification accuracy. Simulation of an analog computing EEG seizure detection block shows a classification accuracy of 91%. The proposed modeling approach will significantly reduce design time and complexity of large analog computing systems. Two feature extraction approaches are also compared for an analog computing architecture. 

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   https://doi.org/10.1109/ISCAS45731.2020.9180802  

   Zhang, Yuqing ; Mirchandani, Nikita ; Onabajo, Marvin ; Shrivastava, Aatmesh ( October 2020 , 2020 IEEE International Symposium on Circuits and Systems (ISCAS))

   null (Ed.)
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   https://doi.org/10.1109/ISCAS48785.2022.9937707  

   Tang, Xiaochen ; Liu, Shanshan ; Che, Wenjie ; Tang, Wei ( May 2022 , IEEE International Symposium on Circuits and Systems (ISCAS))