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.