Neuromorphic Computing

Explainer

Neuromorphic Computing explained

Neuromorphic computing builds chips that copy some features of the brain. Instead of a central processor and separate memory, a neuromorphic chip has many small units that act like neurons, each with its own local memory, joined by connections that act like synapses.

Most designs use spiking neural networks (SNNs). Neurons send short pulses, called spikes, only when something changes. Between spikes they do almost nothing, so the chip uses energy in proportion to activity rather than running all the time.

How it differs from the usual approach

A CPU or GPU runs a clock and processes data in regular steps, whether or not the input changed. A neuromorphic chip is event-driven and keeps memory next to compute, which avoids much of the energy spent moving data. It works well on sparse, changing signals such as sensor data. It is harder to program, because the common deep-learning tools are built for conventional networks, and models often need conversion or special training.

Where it is used

• Always-on sensing in low-power devices, such as keyword and gesture detection.
• Event cameras, which report only the pixels that change.
• Robotics and drone control where energy and response time are limited.
• Research into new learning methods and brain modelling.
• Optimisation and pattern-matching experiments on research systems.

What to watch

• IBM's TrueNorth (2014) held about one million neurons. Intel's Loihi chips followed, and in 2024 Intel described Hala Point, a system built from 1,152 Loihi 2 chips with roughly 1.15 billion neurons.
• A few commercial neuromorphic chips target edge sensing, with the market still small.
• Software tools are the main barrier. Mainstream machine-learning frameworks do not map directly onto spiking hardware.
• It remains unproven whether neuromorphic designs can match the accuracy of conventional deep learning on large tasks while keeping their energy advantage.

Frequently asked questions