Spiking Neural Networks (SNNs) have been widely praised for their high energy efficiency and immense potential. However, comprehensive research that critically contrasts and correlates SNNs with quantized Artificial Neural Networks (ANNs) remains scant, often leading to skewed comparisons lacking fairness towards ANNs. This paper introduces a unified perspective, illustrating that the time steps in SNNs and quantized bit-widths of activation values present analogous representations. Building on this, we present a more pragmatic and rational approach to estimating the energy consumption of SNNs. Diverging from the conventional Synaptic Operations (SynOps), we champion the "Bit Budget" concept. This notion permits an intricate discourse on strategically allocating computational and storage resources between weights, activation values, and temporal steps under stringent hardware constraints. Guided by the Bit Budget paradigm, we discern that pivoting efforts towards spike patterns and weight quantization, rather than temporal attributes, elicits profound implications for model performance. Utilizing the Bit Budget for holistic design consideration of SNNs elevates model performance across diverse data types, encompassing static imagery and neuromorphic datasets. Our revelations bridge the theoretical chasm between SNNs and quantized ANNs and illuminate a pragmatic trajectory for future endeavors in energy-efficient neural computations.

翻译：脉冲神经网络因其高能效和巨大潜力而广受赞誉。然而，目前缺乏对SNN与量化人工神经网络进行全面对比与关联的深入研究，这往往导致不公平的比较，未能公正对待ANN。本文提出统一视角，阐明SNN中的时间步长与激活值的量化位宽具有相似的表示形式。基于此，我们提出一种更实用、更合理的SNN能耗估算方法。与传统的突触操作不同，我们倡导"比特预算"概念。这一概念允许在严格的硬件约束下，就权重、激活值和时序步骤之间战略性地分配计算与存储资源进行深入讨论。在比特预算范式的指导下，我们发现将重点转向脉冲模式和权重量化而非时序属性，对模型性能具有深远影响。利用比特预算对SNN进行整体设计考量，可提升模型在包括静态图像和神经形态数据集在内的多种数据类型上的性能。我们的发现不仅弥合了SNN与量化ANN之间的理论鸿沟，也为未来节能神经计算的研究指明了务实方向。