FF-INT8: Efficient Forward-Forward DNN Training on Edge Devices with INT8 Precision

Backpropagation has been the cornerstone of neural network training for decades, yet its inefficiencies in time and energy consumption limit its suitability for resource-constrained edge devices. While low-precision neural network quantization has been extensively researched to speed up model infere...

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Bibliographic Details

Published in:	2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7
Main Authors:	Ma, Jingxiao, Panda, Priyadarshini, Reda, Sherief
Format:	Conference Proceeding
Language:	English
Published:	IEEE 22.06.2025
Subjects:	Accuracy Backpropagation Energy conservation Energy consumption Energy efficiency Inference algorithms Low power Lowprecision training Neural network quantization Neural networks Quantization (signal) Resource-constrained devices Training
Online Access:	Get full text
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