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Table 4: Comparison of the computational costs between SNN and ANN. The ratio of their required total synaptic operations (SynOps(SNN) / SynOps(ANN)) is reported. It worth mentioning that ANNs use more costly MAC operations than the AC operations used in the SNNs.
['Utterance Index', '1', '2', '3', '4', '5', '[BOLD] Avg. SynOps Ratio']
In addition to the promising modeling capability, the SNN-based ASR systems can achieve unprecedented performance gain when implemented on the low-power neuromorphic chips. In this section, we shed light on this prospect by comparing the energy efficiency of ANN- and SNN-based acoustic models. Given that data movements are the most energy-consuming operations for data-driven AI applications, we calculate the average synaptic operations on 5 randomly chosen utterances from the TIMIT corpus and report the ratio of average synaptic operations required per feature classification (SynOps(SNN) / SynOps(ANN)). Among the three features explored in this experiment, it is interesting to note the FMLLR feature achieves the lowest average spike rate. It is likely due to the more discriminative nature of the speaker-dependent feature, while it worth to note that the FMLLR feature is not always available in all ASR scenarios. Although the average number of synaptic operations required for SNNs that using MFCC and FBANK features are slightly higher than the ANNs, the AC operations performed on SNNs are much cheaper than the MAC operations required for ANNs. While the actual energy savings for SNN-based acoustic models are dependent on the chip architectures and materials used, which is outside the scope of this work.