Presentation
FF-INT8: Efficient Forward-Forward DNN Training on Edge Devices with INT8 Precision
DescriptionBackpropagation, while foundational to neural network training, is inefficient for resource-constrained edge devices due to its high time and energy consumption. While low-precision quantization has been explored for inference speed-up, its use in training remains underexplored. The Forward-Forward (FF) algorithm offers an alternative by replacing the backward pass with an additional forward pass, reducing memory and computation. This paper introduces an INT8 quantized training approach using FF's layer-by-layer strategy to stabilize gradient quantization and proposes a "look-afterward" scheme to improve accuracy. Experiments a edge device show 4.6% faster training, 8.3% energy savings, and 27.0% reduced memory usage, with competitive accuracy.
Event Type
Research Manuscript
TimeTuesday, June 242:30pm - 2:45pm PDT
Location3000, Level 3
AI1: AI/ML Algorithms
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