A Δ-Based Spike Sorting SoC with End-to-End Implementation of Event-Driven Binary Autoencoder Neural Network in Analog CIM Achieving 94.54% Accuracy and 3.11μW/ch

Edward Choi; Vincent Lukito; Injun Choi; Seoyoung Lee; Ik Joon Chang; Sohmyung Ha; Minkyu Je

doi:10.1109/VLSITechnologyandCir46783.2024.10631554

A Δ-Based Spike Sorting SoC with End-to-End Implementation of Event-Driven Binary Autoencoder Neural Network in Analog CIM Achieving 94.54% Accuracy and 3.11μW/ch

Edward Choi, Vincent Lukito, Injun Choi, Seoyoung Lee, Ik Joon Chang, Sohmyung Ha, Minkyu Je

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The proposed Δ -based spike sorting (SS) SoC is the first on-chip implementation of an analog computing-in-memory (CIM) binary autoencoder neural network (B-AENN) feature extraction with enhanced spike detection adopting Δ -spikes, resulting in the highest on-chip SS classification accuracy of 94.54%. It also allows to reduce the digital data transmission rate by 48.8× compared to prior SS systems.

Original language	English (US)
Title of host publication	2024 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2024
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350361469
DOIs	https://doi.org/10.1109/VLSITechnologyandCir46783.2024.10631554
State	Published - 2024
Event	2024 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2024 - Honolulu, United States Duration: Jun 16 2024 → Jun 20 2024

Publication series

Name	Digest of Technical Papers - Symposium on VLSI Technology
ISSN (Print)	0743-1562

Conference

Conference	2024 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2024
Country/Territory	United States
City	Honolulu
Period	6/16/24 → 6/20/24

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/VLSITechnologyandCir46783.2024.10631554

Cite this

Choi, E., Lukito, V., Choi, I., Lee, S., Chang, I. J., Ha, S., & Je, M. (2024). A Δ-Based Spike Sorting SoC with End-to-End Implementation of Event-Driven Binary Autoencoder Neural Network in Analog CIM Achieving 94.54% Accuracy and 3.11μW/ch. In 2024 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2024 (Digest of Technical Papers - Symposium on VLSI Technology). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VLSITechnologyandCir46783.2024.10631554

A Δ-Based Spike Sorting SoC with End-to-End Implementation of Event-Driven Binary Autoencoder Neural Network in Analog CIM Achieving 94.54% Accuracy and 3.11μW/ch. / Choi, Edward; Lukito, Vincent; Choi, Injun et al.
2024 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2024. Institute of Electrical and Electronics Engineers Inc., 2024. (Digest of Technical Papers - Symposium on VLSI Technology).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Choi, E, Lukito, V, Choi, I, Lee, S, Chang, IJ, Ha, S & Je, M 2024, A Δ-Based Spike Sorting SoC with End-to-End Implementation of Event-Driven Binary Autoencoder Neural Network in Analog CIM Achieving 94.54% Accuracy and 3.11μW/ch. in 2024 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2024. Digest of Technical Papers - Symposium on VLSI Technology, Institute of Electrical and Electronics Engineers Inc., 2024 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2024, Honolulu, United States, 6/16/24. https://doi.org/10.1109/VLSITechnologyandCir46783.2024.10631554

Choi E, Lukito V, Choi I, Lee S, Chang IJ, Ha S et al. A Δ-Based Spike Sorting SoC with End-to-End Implementation of Event-Driven Binary Autoencoder Neural Network in Analog CIM Achieving 94.54% Accuracy and 3.11μW/ch. In 2024 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2024. Institute of Electrical and Electronics Engineers Inc. 2024. (Digest of Technical Papers - Symposium on VLSI Technology). doi: 10.1109/VLSITechnologyandCir46783.2024.10631554

Choi, Edward ; Lukito, Vincent ; Choi, Injun et al. / A Δ-Based Spike Sorting SoC with End-to-End Implementation of Event-Driven Binary Autoencoder Neural Network in Analog CIM Achieving 94.54% Accuracy and 3.11μW/ch. 2024 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2024. Institute of Electrical and Electronics Engineers Inc., 2024. (Digest of Technical Papers - Symposium on VLSI Technology).

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abstract = "The proposed Δ -based spike sorting (SS) SoC is the first on-chip implementation of an analog computing-in-memory (CIM) binary autoencoder neural network (B-AENN) feature extraction with enhanced spike detection adopting Δ -spikes, resulting in the highest on-chip SS classification accuracy of 94.54%. It also allows to reduce the digital data transmission rate by 48.8× compared to prior SS systems.",

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A Δ-Based Spike Sorting SoC with End-to-End Implementation of Event-Driven Binary Autoencoder Neural Network in Analog CIM Achieving 94.54% Accuracy and 3.11μW/ch

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