在分布式深度学习中推进 Forward-Forward
Going Forward-Forward in Distributed Deep Learning
AI总结：
本文研究 FF 算法在分布式深度学习中的并行实现，通过双前向传播替代反向传播，在四节点四层网络上于 MNIST 实现 3.75 倍加速且不损失精度。
摘要：
我们提出一种分布式深度学习的新方法，利用 Geoffrey Hinton 的 Forward-Forward（FF）算法来加速分布式计算环境中的神经网络训练。不同于依赖前向传播和反向传播的传统方法，FF 算法采用双前向传播策略，与传统反向传播过程有显著区别。这一新方法更贴近人脑的处理机制，有可能提供一种更高效且具有生物合理性的神经网络训练途径。我们的研究探索了 FF 算法在分布式环境中的不同实现方式，以考察其并行化能力。尽管最初的 FF 算法关注的是其达到反向传播算法性能的能力，但这里的并行化旨在减少训练时间和资源消耗，从而解决深度神经网络训练相关的训练时间过长问题。我们的评估表明，在使用四个计算节点训练一个四层网络时，在 MNIST 数据集上实现了 3.75 倍加速，且没有损失准确率。将 FF 算法集成到分布式深度学习中代表了该领域的重要进步，并有可能彻底改变分布式环境中神经网络的训练方式。
英文摘要：
We introduce a new approach in distributed deep learning, utilizing Geoffrey Hinton's Forward-Forward (FF) algorithm to speed up the training of neural networks in distributed computing environments. Unlike traditional methods that rely on forward and backward passes, the FF algorithm employs a dual forward pass strategy, significantly diverging from the conventional backpropagation process. This novel method aligns more closely with the human brain's processing mechanisms, potentially offering a more efficient and biologically plausible approach to neural network training. Our research explores different implementations of the FF algorithm in distributed settings, to explore its capacity for parallelization. While the original FF algorithm focused on its ability to match the performance of the backpropagation algorithm, the parallelism aims to reduce training times and resource consumption, thereby addressing the long training times associated with the training of deep neural networks. Our evaluation shows a 3.75 times speed up on MNIST dataset without compromising accuracy when training a four-layer network with four compute nodes. The integration of the FF algorithm into distributed deep learning represents a significant step forward in the field, potentially revolutionizing the way neural networks are trained in distributed environments.
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