Neuromorphic Computing

What is Neuromorphic Computing? Hardware that mimics biological neural networks using spiking neurons and event-driven computation.

Key Concepts

Neuromorphic Chips

Benefits

Spiking Neural Networks (SNNs)

# Using snnTorch
import snntorch as snn

class SpikingNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(784, 500)
        self.lif1 = snn.Leaky(beta=0.9)  # Leaky integrate-and-fire
        self.fc2 = nn.Linear(500, 10)
        self.lif2 = snn.Leaky(beta=0.9)

    def forward(self, x, mem1, mem2):
        cur1 = self.fc1(x)
        spk1, mem1 = self.lif1(cur1, mem1)
        cur2 = self.fc2(spk1)
        spk2, mem2 = self.lif2(cur2, mem2)
        return spk2, mem1, mem2

Intel Loihi

# Using Lava framework
import lava.lib.dl.netx as netx

# Load trained SNN
net = netx.hdf5.Network(net_config="trained_network.net")

# Deploy to Loihi
from lava.lib.dl.netx.utils import NetDict
loihi_net = NetDict(net)

Use Cases

Challenges

Current Limitations

• Not yet competitive for large models
• Limited commercial availability
• Requires new thinking about algorithms

Best Practices

• Consider for extreme power constraints
• Good for temporal/event-driven data
• Use ANN-to-SNN conversion
• Start with simulators before hardware