用脉冲网络重新理解AI，有望实现千倍能效提升
Spiking Manifesto
- 将主流AI模型映射为脉冲神经网络，用脉冲时序编码信息
- 脉冲网络通过时序关联实现组合式编码，效率比传统模型高千倍
- 适合追求极致能效的边缘计算与类脑芯片设计者
当前计算机在几乎所有任务中都比大脑更快、更节能。例如，计算器的数值运算远优于人类。然而，现代人工智能模型的能效却比大脑低一千倍。这些模型依赖日益庞大的人工神经网络（ANN）提升编码能力，需借助GPU进行大规模矩阵乘法。相比之下，大脑的脉冲神经网络（SNN）通过脉冲的多时序同步（polychronization）实现指数级增长的编码容量，能耗极低。本文提出一种范式，将主流AI模型转化为脉冲网络并解释其脉冲活动为自然界的查找表实现。这为构建体积更小但组合编码能力爆炸性增长的新架构开辟了道路，有望带来千倍性能提升。代码已开源：https://github.com/izhikevich/SNN
原文摘要 · Abstract (English)
Practically everything computers do is better, faster, and more power-efficient than the brain. For example, a calculator performs numerical computations more energy-efficiently than any human. Yet modern AI models are a thousand times less efficient than the brain. These models rely on larger and larger artificial neural networks (ANNs) to boost their encoding capacity, requiring GPUs to perform large-scale matrix multiplications. In contrast, the brain's spiking neural networks (SNNs) exhibit factorially explosive encoding capacity and compute through the polychronization of spikes rather than explicit matrix-vector products, resulting in lower energy requirements. This manifesto proposes a paradigm for framing popular AI models in terms of spiking networks and polychronization, and for interpreting spiking activity as nature's way of implementing look-up tables. This suggests a path toward converting AI models into a novel class of architectures with much smaller size yet combinatorially large encoding capacity, offering the promise of a thousandfold improvement in performance. Code is available at https://github.com/izhikevich/SNN
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