Approximation-Aware Training for Efficient Neural Network Inference on MRAM Based CiM Architecture
Convolutional neural networks (CNNs), despite their broad applications, are constrained by high computational and memory requirements. Existing compression techniques often neglect approximation errors incurred during training. This work proposes approximation-aware-training, in which group of weigh...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Open Journal of Nanotechnology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10819260/ |
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