August 2023

Conference Paper

A 3D Implementation of Convolutional Neural Network for Fast Inference

By:
Miniskar, Narasinga Rao ; Vanna iampikul, Pruek; Young, Aaron R; Kyu Lim, Sung; Liu, Frank Y; Yoo, Jieun; Mills, Corrinne; Tran, Nhan; Fahim, Farah; Vetter, Jeffrey S
Page Number:
1-5
Book Title:
IEEE International Symposium on Circuits and Systems (ISCAS)
Publication Date:
August 2023
Publisher Location:
IEEE, New Jersey, United States of America
Conference Name:
2023 IEEE International Symposium on Circuits and Systems (ISCAS)
Conference Location:
Monterey, California, United States of America
Conference Sponsor:
IEEE
View DOI Listing:
https://doi.org/10.1109/ISCAS46773.2023.10181622

Abstract

Low latency inference has many applications in edge machine learning. In this paper, we present a run-time configurable convolutional neural network (CNN) inference ASIC design for low-latency edge machine learning. By implementing a 5-stage pipelined CNN inference model in a 3D ASIC technology, we demonstrate that the model distributed on two dies utilizing face-to-face (F2F) 3D integration achieves superior performance. Our experimental results show that the design based on 3D integration achieves 43% better energy-delay product when compared to the traditional 2D technology.

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