Power consumption plays an important role in on-device streaming speech recognition, as it has a direct impact on the user experience. This study delves into how weight parameters in speech recognition models influence the overall power consumption of these models. We discovered that the impact of weight parameters on power consumption varies, influenced by factors including how often they are invoked and their placement in memory. Armed with this insight, we developed design guidelines aimed at optimizing on-device speech recognition models. These guidelines focus on minimizing power use without substantially affecting accuracy. Our method, which employs targeted compression based on the varying sensitivities of weight parameters, demonstrates superior performance compared to state-of-the-art compression methods. It achieves a reduction in energy usage of up to 47% while maintaining similar model accuracy and improving the real-time factor.

翻译：功耗在设备端流式语音识别中扮演着重要角色，因为它直接影响用户体验。本研究深入探讨了语音识别模型中的权重参数如何影响模型的整体功耗。我们发现，权重参数对功耗的影响各不相同，其受调用频率和内存位置等因素的影响。基于这一认识，我们制定了一套旨在优化设备端语音识别模型的设计指南。这些指南侧重于在不显著影响准确性的前提下最大程度地降低功耗。我们的方法根据权重参数的不同敏感度进行针对性压缩，与最先进的压缩方法相比，表现出更优越的性能。它在保持相似模型准确性并提升实时性能因子的同时，实现了高达47%的能耗降低。