QoS-Aware Inference Acceleration Using Adaptive Depth Neural Networks

Abstract

While deep neural networks (DNNs) have brought revolutions to many intelligent services and systems, the deployment of high-performing models for real-world applications faces challenges posed by resource constraints and diverse operating environments. While existing methods such as model compression combined with inference accelerators have enhanced the efficiency of deep neural networks, they are not dynamically adaptable to dynamically changing resource conditions since they provide static accuracy-efficiency trade-offs. Further, since they are not aware of performance requirements, such as desired inference latency, they are not able to provide robust and effective performance under unpredictable workloads. This paper introduces a holistic solution to address this challenge, consisting of two key components: adaptive depth neural networks and the Quality of Service (QoS)-aware inference accelerator. The adaptive depth neural networks exhibit the ability to scale computation instantly with minimal impact on accuracy, utilizing a novel architectural pattern and training algorithm. Complementing this, the QoS-aware inference accelerator employs a feedback control loop, adapting network depth dynamically to meet desired inference latency. Experimental results demonstrate that the proposed adaptive depth networks outperform non-adaptive counterparts, achieving up to 38% dynamic acceleration via depth adaption, with a marginal accuracy loss of 1.5%. Furthermore, the QoS-aware inference accelerator successfully controls network depth at runtime, ensuring robust performance in unpredictable environments.

Keywords

• Deep learning
• inference acceleration
• deep neural networks
• feedback control
• real-time
• energy efficiency