In this article we share the principles behind this reference implementation to provide generalizable guidance to developers on optimizing their models for ANE execution. Increasing the adoption of on-device ML deployment will also benefit user privacy, since data for inference workloads remains on-device, not on the server. It will help developers minimize the impact of their ML inference workloads on app memory, app responsiveness, and device battery life. This implementation is specifically optimized for the Apple Neural Engine (ANE), the energy-efficient and high-throughput engine for ML inference on Apple silicon. This year at WWDC 2022, Apple is making available an open-source reference PyTorch implementation of the Transformer architecture, giving developers worldwide a way to seamlessly deploy their state-of-the-art Transformer models on Apple devices. This architecture helps enable experiences such as panoptic segmentation in Camera with HyperDETR, on-device scene analysis in Photos, image captioning for accessibility, machine translation, and many others. An increasing number of the machine learning (ML) models we build at Apple each year are either partly or fully adopting the Transformer architecture.
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