Editing Neuromorphic (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
Standard deep learning on GPUs consumes enormous energy: a large language model inference can require hundreds of watts. The human brain performs equivalent computations on ~20 watts. Neuromorphic computing aims to close this gap by mimicking how the brain processes information efficiently.

'''The von Neumann bottleneck''': Traditional computers constantly shuttle data between separate CPU and RAM — the "memory wall." Neuromorphic systems co-locate computation and memory at each neuron/synapse, eliminating this bottleneck entirely.

'''SNNs vs. ANNs''': Artificial Neural Networks (ANNs) propagate continuous-valued activations through every layer on every forward pass — computationally expensive. Spiking Neural Networks fire discrete spikes only when membrane potential exceeds threshold. Since most neurons are silent at any moment, computation is sparse and event-driven — power is consumed only when a spike occurs, potentially enabling 100-1000× power reduction.

'''The training challenge''': Standard backpropagation doesn't work for SNNs — spike functions are non-differentiable. Solutions:
# Surrogate gradient methods approximate the spike derivative for backprop.
# ANN-to-SNN conversion: train an ANN, convert weights to SNN, calibrate thresholds.
# Biologically plausible rules (STDP) work for simple tasks but struggle with deep networks.

'''Current state''': Neuromorphic hardware exists and runs SNNs with impressive energy efficiency (IBM TrueNorth: 0.07W for image inference). But SNN accuracy typically lags behind ANN equivalents, and programming neuromorphic hardware requires specialized frameworks (PyNN, Norse, SpikingJelly). The field is active but hasn't yet displaced GPU-based inference for most applications.
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