neuromorphic
**Neuromorphic Computing**
**What is Neuromorphic Computing?**
Hardware that mimics biological neural networks using spiking neurons and event-driven computation.
**Key Concepts**
| Concept | Description |
|---------|-------------|
| Spiking neurons | Communicate via discrete spikes |
| Event-driven | Compute only when spikes arrive |
| Local learning | Synaptic plasticity (Hebbian) |
| Temporal coding | Information in spike timing |
**Neuromorphic Chips**
| Chip | Company | Neurons | Synapses |
|------|---------|---------|----------|
| Loihi 2 | Intel | 1M | 120M |
| TrueNorth | IBM | 1M | 256M |
| SpiNNaker 2 | TU Dresden | 10M+ | Programmable |
| Akida | BrainChip | 1.4M | - |
**Benefits**
| Benefit | Impact |
|---------|--------|
| Power efficiency | 100-1000x vs GPU |
| Latency | Real-time processing |
| Always-on | Low standby power |
| Edge perfect | Sensors, robotics |
**Spiking Neural Networks (SNNs)**
```python
# 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**
```python
# 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**
| Use Case | Why Neuromorphic |
|----------|------------------|
| Robotics | Real-time, low power |
| Edge sensors | Always-on, efficient |
| Event cameras | Natural spike input |
| Anomaly detection | Temporal patterns |
**Challenges**
| Challenge | Status |
|-----------|--------|
| Training | Converting from ANNs common |
| Ecosystem | Maturing frameworks |
| Accuracy | Approaching ANNs |
| Programming | Specialized skills needed |
**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