How can TinyML optimize multi-agent inference for mining machinery?
Enhancing Predictive Maintenance in Mining Mobile Machinery through a TinyML-enabled Hierarchical Inference Network
This paper proposes a hierarchical inference framework called ESN-PdM for predictive maintenance of mining machinery. It uses TinyML to enable on-device, on-gateway, and cloud-based inference, dynamically switching between them based on accuracy, latency, and energy needs.
While not explicitly a multi-agent system, ESN-PdM's dynamic inference switching, based on factors like data accuracy and resource availability, is relevant to LLM-based multi-agent systems. The adaptive heuristics and dynamic resource allocation principles could inform the design of agents collaborating in resource-constrained environments, dynamically selecting where to execute complex LLM computations. The hierarchical structure and communication patterns (MQTT, BLE) offer a blueprint for multi-agent coordination. This system's focus on efficiency in computationally intensive tasks, achieved via TinyML, mirrors the need to optimize LLM execution in distributed multi-agent scenarios.