Chronological feed of everything captured from Chip Huyen.
This paper provides the first systematic evaluation of deploying spiking neural network (SNN) workloads within a Kubernetes-based container orchestration framework (K3d) in a virtual edge environment. SNN inference proves highly sensitive to resource constraints — modest CPU restriction to 0.5 cores causes catastrophic throughput degradation — while classification accuracy remains unaffected. A key architectural finding is that Kubernetes' default round-robin load balancing is poorly suited to long-running SNN inference workloads, producing significant tail latency under replica scaling. The study establishes a baseline for neuromorphic edge deployment and surfaces concrete gaps in orchestration configuration, routing strategy, and memory management.