Efficient Deep Speech Understanding at the Edge

In contemporary speech understanding (SU), a sophisticated pipeline is employed, encompassing the ingestion of streaming voice input. The pipeline executes beam search iteratively, invoking a deep neural network to generate tentative outputs (referred to as hypotheses) in an autoregressive manner. Periodically, the pipeline assesses attention and Connectionist Temporal Classification (CTC) scores. This paper aims to enhance SU performance on edge devices with limited resources. Adopting a hybrid strategy, our approach focuses on accelerating on-device execution and offloading inputs surpassing the device's capacity. While this approach is established, we tackle SU's distinctive challenges through innovative techniques: (1) Late Contextualization: This involves the parallel execution of a model's attentive encoder during input ingestion. (2) Pilot Inference: Addressing temporal load imbalances in the SU pipeline, this technique aims to mitigate them effectively. (3) Autoregression Offramps: Decisions regarding offloading are made solely based on hypotheses, presenting a novel approach. These techniques are designed to seamlessly integrate with existing speech models, pipelines, and frameworks, offering flexibility for independent or combined application. Collectively, they form a hybrid solution for edge SU. Our prototype, named XYZ, has undergone testing on Arm platforms featuring 6 to 8 cores, demonstrating state-of-the-art accuracy. Notably, it achieves a 2x reduction in end-to-end latency and a corresponding 2x decrease in offloading requirements.