Tutorial 66: SNN Compiler Optimization Passes

Optimize SNN computation graphs before hardware deployment.

Build a Graph

from sc_neurocore.snn_optimizer import SNNGraph, LayerNode, optimize

graph = SNNGraph(layers=[
    LayerNode("hidden1", 784, 256, weights_h1, firing_rates=rates_h1),
    LayerNode("hidden2", 256, 64, weights_h2, firing_rates=rates_h2),
    LayerNode("output", 64, 10, weights_out, firing_rates=rates_out),
])

Run All Passes

optimized, report = optimize(graph)
print(report.summary())
# SNN Optimizer: 215,104 -> 142,890 params (1.51x compression)

Available Passes

Pass	What It Does
dead_neuron_elimination	Remove neurons that never fire
layer_fusion	Merge adjacent silent layers
redundancy_elimination	Merge neurons with identical weights

Selective Passes

optimized, report = optimize(graph, passes=["dead_neuron_elimination"])