arXiv:2606.28600v1 Announce Type: cross Abstract: Neuromorphic and edge computing research has focused on reducing the inference cost of neural network controllers, yet in physical closed-loop systems the actuator can rival or exceed an efficient controller in energy. An efficient controller is therefore necessary but not sufficient, because the actuator becomes the cost worth reducing once inference no longer dominates it. Here, we introduce energy-aware learning, an approach that incorporates actuator energy directly into the reinforcement learning reward, and demonstrate it in closed-loop d
The increasing focus on energy efficiency in AI and computing, particularly for neuromorphic and edge systems, makes this research highly relevant as computational demands grow.
This development addresses a critical bottleneck in deploying advanced AI in physical systems by directly optimizing for actuator energy, which is often overlooked but significant.
The explicit incorporation of actuator energy into reinforcement learning rewards shifts the paradigm toward truly energy-aware closed-loop control systems, moving beyond just controller efficiency.
- · Neuromorphic computing companies
- · Medical device manufacturers (e.g., deep brain stimulation)
- · Edge AI providers
- · Energy-efficient robotics
- · Developers of energy-inefficient AI systems
- · Traditional control systems lacking energy awareness
More energy-efficient and longer-lasting deep brain stimulation devices become feasible.
The methodology extends to other closed-loop physical systems, significantly reducing their operational energy footprint.
Reduced energy consumption in critical medical and robotic applications leads to broader adoption and new use cases where power constraints were previously prohibitive.
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Read at arXiv cs.LG