The rapid evolution of Artificial Intelligence (AI) and Machine Learning (ML) has significantly heightened computational demands, particularly for inference-serving workloads. While traditional cloud-based deployments offer scalability, they face challenges such as network congestion, high energy consumption, and privacy concerns. In contrast, edge computing provides low-latency and sustainable alternatives but is constrained by limited computational resources. In this work, we introduce SynergAI , a novel framework designed for performance- and architecture-aware inference serving across heterogeneous edge-to-cloud infrastructures. Built upon a comprehensive performance characterization of modern inference engines, SynergAI integrates a combination of offline and online decision-making policies to deliver intelligent, lightweight, and architecture-aware scheduling. By dynamically allocating workloads across diverse hardware architectures, it effectively minimizes Quality of Service (QoS) violations. We implement SynergAI within a Kubernetes-based ecosystem and evaluate its efficiency. Our results demonstrate that architecture-driven inference serving enables optimized and architecture-aware deployments on emerging hardware platforms, achieving an average reduction of 2.4 × in QoS violations compared to a State-of-the-Art (SotA) solution.
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Foteini Stathopoulou
Aggelos Ferikoglou
Manolis Katsaragakis
ACM Transactions on Embedded Computing Systems
KU Leuven
National Technical University of Athens
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Stathopoulou et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69be37096e48c4981c676529 — DOI: https://doi.org/10.1145/3800959