Biological inspiration
Neurons integrate charge and fire brief pulses. Copy that and computation becomes a conversation of events rather than a march of clock cycles.
Explore the biologyComputing, rebuilt from the neuron up
Chips that think in spikes, not clock cycles
Your brain recognises a face on twenty watts. A data-centre GPU needs seven hundred to approximate the same trick. Neuromorphic computing closes that gap by building silicon that behaves like neural tissue: event-driven, massively parallel, and with memory living inside the computation itself.
input layerspike propagationoutput
20 W
Human brain power budget
86 B
Neurons in a human cortex
~1000x
Reported efficiency gain on sparse tasks
1.15 B
Neurons in Intel's Hala Point system
Why it matters
Three ideas that make brain-inspired silicon different
Neuromorphic engineering is not a faster processor. It is a different bargain: give up exactness and global synchrony, and get extraordinary efficiency on messy, real-world sensory data.
Architecture rethought
No bus, no bottleneck. Memory sits inside every core, so weights never travel and idle silicon costs almost nothing.
Compare architecturesSee it spike
Adjust neurons, threshold, input rate and synaptic weight, then watch a live raster plot and an energy budget respond in real time.
Open the simulatorThe core intuition
Silence is the feature
A conventional processor is a metronome: it ticks billions of times a second, and every tick costs energy whether or not there is work to do. A spiking chip is a conversation. Nothing is said until something changes.
- 1Sensors produce events, not frames — a pixel reports only when its brightness changes.
- 2Neurons accumulate those events and fire only on crossing a threshold.
- 3Weights are stored where they are used, so no data crosses a memory bus.
- 4Learning happens locally, from the relative timing of nearby spikes.
Energy per operation, roughly
DRAM read (64-bit)100%
GPU multiply-accumulate34%
SRAM cache read12%
Neuromorphic synaptic event3%
Relative, illustrative values. The headline is the ordering: moving a number usually costs far more than computing with it.
Build the intuition by playing with a real neuron model
The simulator runs a leaky integrate-and-fire population live in your browser. Move four sliders and watch spiking activity, firing rate and energy consumption respond instantly — with a plain-language explanation of every control.
Launch the simulator