Learning Using a Single Forward Pass

• URL: http://arxiv.org/abs/2402.09769v2
• Date: Mon, 10 Mar 2025 06:32:41 GMT
• Title: Learning Using a Single Forward Pass
• Authors: Aditya Somasundaram, Pushkal Mishra, Ayon Borthakur,
• Abstract summary: We propose a learning algorithm to overcome the limitations of a traditional backpropagation in resource-constrained environments: Solo Pass Embedded Learning Algorithm (SPELA)<n>SPELA is equipped with rapid learning capabilities and operates with local loss functions to update weights, significantly saving on resources allocated to the propagation of gradients and storing computational graphs while being sufficiently accurate.<n>Our results indicate that SPELA can be an ideal candidate for learning in resource-constrained edge AI applications.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a learning algorithm to overcome the limitations of a traditional backpropagation in resource-constrained environments: Solo Pass Embedded Learning Algorithm (SPELA). SPELA is equipped with rapid learning capabilities and operates with local loss functions to update weights, significantly saving on resources allocated to the propagation of gradients and storing computational graphs while being sufficiently accurate. Consequently, SPELA can closely match backpropagation with less data, computing, storage, and power. Moreover, SPELA can effectively fine-tune pre-trained image recognition models for new tasks. Our results indicate that SPELA can be an ideal candidate for learning in resource-constrained edge AI applications.

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