Neurarray
The brain is not a processor. It is an array — a distributed mesh of electrochemical nodes, each connected, each firing, each contributing to intelligence that no single neuron possesses. Neurarray names this principle. From silicon synapse to planetary inference.
NeuRRAM, NeuroArray, and analog in-memory compute architectures eliminate the von Neumann bottleneck by placing computation inside the memory array itself. The spike is the signal. The array is the processor. Energy drops by orders of magnitude.
Spiking neural networks arrayed across edge hardware — autonomous vehicles, industrial robots, wearable sensors — run inference locally without cloud dependency. The array thinks where it acts.
Neuromorphic vision sensors — event cameras, retinal arrays — encode light as asynchronous spike trains rather than frames. Microsecond latency. Orders of magnitude less data. The retina is an array. The array sees faster.
Intelligence is not a property of any single node. It is a property of how nodes are arranged.
Every architecture that has produced genuine intelligence — biological or artificial — has been an array. Cortical columns. Transformer attention heads. Convolutional filter banks. Resistive memory crossbars. The principle recurs because it is not a design choice. It is the only way intelligence scales.
Working on
neural array
architecture?
Neuromorphic computing, in-memory inference, spiking neural networks, event-based sensing, or distributed edge intelligence — we want to hear from researchers and builders working at any scale of the array.