  In this paper, we propose a dynamic cascaded encoder Automatic Speech
Recognition (ASR) model, which unifies models for different deployment
scenarios. Moreover, the model can significantly reduce model size and power
consumption without loss of quality. Namely, with the dynamic cascaded encoder
model, we explore three techniques to maximally boost the performance of each
model size: 1) Use separate decoders for each sub-model while sharing the
encoders; 2) Use funnel-pooling to improve the encoder efficiency; 3) Balance
the size of causal and non-causal encoders to improve quality and fit
deployment constraints. Overall, the proposed large-medium model has 30%
smaller size and reduces power consumption by 33%, compared to the baseline
cascaded encoder model. The triple-size model that unifies the large, medium,
and small models achieves 37% total size reduction with minimal quality loss,
while substantially reducing the engineering efforts of having separate models.
