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English(EN) Energy-Efficient Deep Learning Without Backpropagation: A Rigorous Hardware-Validated Benchmarking Study of Forward-Only Algorithms

仅前向的深度学习算法显示出混合的能源效率

作者 PulseAugur 编辑部 · [1 个来源] ·

一项新近发表在arXiv上的研究对仅前向的深度学习算法与传统反向传播进行了基准测试。研究发现，虽然像Forward-Forward和Cascaded Forward等一些仅前向的方法比反向传播的能源效率低，但Mono-Forward显示出潜力。在某些配置下，Mono-Forward使用的能源和内存少于反向传播，并且在更少的迭代次数下达到可比的测试准确率，尽管它需要更多的迭代次数和能源才能收敛。 AI

影响 通过探索反向传播的替代方案来研究深度学习中的潜在节能效果，这可能会影响未来的硬件和模型训练效率。

排序理由 该集群包含一篇详细介绍深度学习算法基准研究的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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仅前向的深度学习算法显示出混合的能源效率

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该集群包含一篇详细介绍深度学习算法基准研究的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Przemys{\l}aw Spyra, Witold Dzwinel ·

    无需反向传播的节能深度学习：前向传播算法的严格硬件验证基准研究

    arXiv:2511.01061v2 Announce Type: replace-cross Abstract: Backpropagation (BP) powers modern deep learning, but it must hold every activation in memory until a global backward sweep consumes it, and that sweep locks each layer until the one above has finished. Forward-only algori…

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