A hierarchical adaptive controller for edge ML deploys cascades of specialized models with local drift tracking to cut latency by up to 2.45x and energy by 2.86x under distribution shifts while keeping accuracy loss below 4%.
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Industrial systems use machine learning models on many different devices that must respond quickly and use little power and memory. These models can be made dynamic so they change how much computation they do depending on the situation, which saves energy on average. However, the settings that control this tradeoff are usually chosen using a set of training data that is assumed to look like the data seen later. In real use this assumption often fails because the world changes, causing the system to perform worse than a simple fixed model. The authors create a two-part control system to fix this. A central scheduler decides for each edge device which chain of small specialized models plus one general model to use, while making sure the chain always meets the strict time and memory limits. Then, on each device itself, a local controller watches the incoming data for changes and checks the current hardware resources. It can turn the small specialized models on or off to keep good accuracy and avoid breaking the time limit. This way the device can run for a long time without needing the central scheduler to send new instructions, and it still works if the network to the scheduler is down. They tested this idea on two different data sets where they deliberately made the data look different from the training data. The results showed that on average each prediction took up to 2.45 times less time and used up to 2.86 times less energy than fixed models, while losing less than 4 percent in accuracy. The main new ideas are a way to build the chain of models so the worst-case time is always safe, the two-level controller that reacts to changes, and the tests on real small computers.
Core claim
We evaluate the approach on two datasets under controlled distribution mismatch scenarios, showing average per-inference reductions of latency up to 2.45x and energy up to 2.86x, with less than 4% accuracy drop compared to static baselines.
Load-bearing premise
The local controller can reliably detect and respond to data distribution drifts and hardware resource changes in real time without adding overhead that itself violates latency or energy constraints.
read the original abstract
Industrial systems increasingly depend on Machine Learning (ML), and operate on heterogeneous nodes that must satisfy tight latency, energy, and memory constraints. Dynamic ML models, which reconfigure their computational footprint at runtime, promise high energy efficiency and lower average latency for modest accuracy tradeoffs; however, their deployment is complex due to the additional hyperparameters they rely on. These hyperparameters, controlling the accuracy versus average latency tradeoff, are often tuned on a calibration dataset that must match the test time distribution, an assumption that rarely holds in real-world scenarios, leading to suboptimal operational conditions, possibly below static models. We propose a two-tier adaptive architecture that co-optimizes model and system decisions. At the global level, a scheduler configures and deploys, for each edge node, a cascade of classifiers composed of lightweight specialized models and a generalist fallback, satisfying latency and memory constraints. At the node level, a local controller tracks data drifts and hardware resources, enabling or disabling specialized predictors (SP) to preserve high energy efficiency and avoid latency-constraint violations under varying conditions. This design allows longer operating times without forcing a global redeployment step, and enables efficient execution in case of an unreachable remote global controller. We evaluate the approach on two datasets under controlled distribution mismatch scenarios, showing average per-inference reductions of latency up to 2.45x and energy up to 2.86x, with less than 4% accuracy drop compared to static baselines. Our contributions are:(1) a budgeted SP-cascade formulation that preserves worst-case latency constraints;(2) a hierarchical controller that maintains efficiency under data and resource changes; and (3) an experimental evaluation on embedded hardware.
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Abstract provides no explicit free parameters, axioms, or new entities beyond the described architecture; the approach assumes existence of lightweight models and drift detection capability.
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