Routing-SNN Dashboards

Routing as content-addressed memory — training & benchmark results

Spatial SNN Training Ablation →

Pre-training spatial sparsity vs dense on SHD. Exponential 2D locality (exp2dloc) beats dense at 91.8% sparsity. Live training curves, seed grouping, per-config topology cards.

exp2dloc +3 pp vs dense @ 8% density

Kernel Benchmarks →

V2–V8 CUDA kernel speed: bit-packed inputs, wavefront parallelism, Tensor-Core block-sparse decode. Firing-rate crossover scans and width-scaling sweeps.

V7.1 non-routed decode ~1.73–1.84× dense

Routed SSM (conditional computation) →

Stateful, path-dependent routing on a continuous diagonal-SSM substrate. The 2×2 ablation (router-state × block-state) + MQAR parity + decode speedup. Routing-on-state vs the stateless MoE-Mamba/BlackMamba baseline.

stateful routing ~0.99 @ 12.5% active vs stateless ~0.48

Routed SSM · MQAR (all runs) →

Generalized, auto-discovering MQAR training-curve view: every routed/dense run on one page, one shared filter (router × difficulty × seed × variant) driving all charts, mean±band aggregation across seeds.

hard top-k routed ~0.91 vs dense ~0.77 (11 seeds)