Neuromorphic Computing Architectures for Synthetic-Data AI in IT Infrastructure

Neuromorphic computing offers brain-inspired, event-driven processing with fine-grained parallelism and near-memory computation, making it an attractive architectural direction for large-scale AI in IT infrastructure. We present a concise architecture-focused study anchored in synthetic benchmarks and datasets (e.g., N-MNIST, DVS-like streams), quantifying advantages for sparse, streaming workloads. We formalize spiking neuron dynamics, give an energy-to-solution model, and report fully reproducible synthetic results and figures (spike rasters, energy per inference, latency/throughput, node-level integration). We outline an integration path for heterogeneous nodes (CPU+GPU+NPU), identify software tooling for reproducible evaluation, and summarize open challenges in programmability, scaling, and benchmarking. The paper targets Track 6 (AI in IT Infrastructure) by emphasizing system-level fit, energy proportionality, and deployability in data centers.