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Conformer models maintain a large number of internal states, the vastmajority of which are associated with self-attention layers. With limitedmemory bandwidth, reading these from memory at each inference step can slowdown inference. In this paper, we design an optimized conformer that is smallenough to meet on-device restrictions and has fast inference on TPUs. Weexplore various ideas to improve the execution speed, including replacing lowerconformer blocks with convolution-only blocks, strategically downsizing thearchitecture, and utilizing an RNNAttention-Performer. Our optimized conformercan be readily incorporated into a cascaded-encoder setting, allowing asecond-pass decoder to operate on its output and improve the accuracy whenevermore resources are available. Altogether, we find that these optimizations canreduce latency by a factor of 6.8x, and come at a reasonable trade-off inquality. With the cascaded second-pass, we show that the recognition accuracyis completely recoverable. Thus, our proposed encoder can double as a strongstandalone encoder in on device, and as the first part of a high-performanceASR pipeline.

Practical Conformer: Optimizing size, speed and flops of Conformer for on-Device and cloud ASR