Editing Neuromorphic (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Spiking Neural Network with SpikingJelly:'''
<syntaxhighlight lang="python">
import torch
import torch.nn as nn
from spikingjelly.activation_based import neuron, functional, layer

class SpikingCNN(nn.Module):
    """Simple SNN for image classification."""
    def __init__(self, T=8, num_classes=10):
        super().__init__()
        self.T = T  # Number of time steps
        self.encoder = nn.Sequential(
            layer.Conv2d(1, 32, 3, padding=1), nn.BatchNorm2d(32),
            neuron.LIFNode(tau=2.0),  # Leaky Integrate-and-Fire neuron
            layer.MaxPool2d(2),
            layer.Conv2d(32, 64, 3, padding=1), nn.BatchNorm2d(64),
            neuron.LIFNode(tau=2.0),
            layer.MaxPool2d(2),
        )
        self.classifier = nn.Sequential(
            layer.Flatten(), layer.Linear(64*7*7, 256),
            neuron.LIFNode(tau=2.0),
            layer.Linear(256, num_classes)
        )
        functional.set_step_mode(self, step_mode='m')  # Multi-step mode

    def forward(self, x):
        # x: (B, C, H, W) → repeat over T time steps
        x_seq = x.unsqueeze(0).repeat(self.T, 1, 1, 1, 1)  # (T, B, C, H, W)
        out = self.encoder(x_seq)
        out = self.classifier(out)
        return out.mean(0)  # Average over time steps
</syntaxhighlight>

; Neuromorphic hardware comparison
: '''Intel Loihi 2''' → 1M neurons, on-chip learning, research-focused
: '''IBM TrueNorth''' → 4096 cores, 256M synapses, 70mW at full operation
: '''BrainScaleS''' → Analog neuromorphic, 1000× faster than biological real-time
: '''SpiNNaker 2''' → Manchester, ARM-based, flexible SNN simulation
: '''Akida (BrainChip)''' → Commercial edge neuromorphic chip for inference
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