A 3D Implementation of Convolutional Neural Network for Fast Inference
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.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1997610
- Resource Relation:
- Conference: 2023 IEEE International Symposium on Circuits and Systems (ISCAS) - Monterey, California, United States of America - 5/21/2023 4:00:00 AM-5/25/2023 4:00:00 AM
- Country of Publication:
- United States
- Language:
- English
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