On 25 September 2017, Intel Labs announced Loihi, an experimental neuromorphic chip built to run spiking neural networks and to learn on the chip itself. Where IBM’s earlier TrueNorth ran fixed, pre-trained networks, Loihi’s headline feature was that it could adapt while operating, adjusting its synapses from feedback without a separate training phase on another machine.
The chip integrated 128 neuromorphic cores plus a few conventional x86 cores, fabricated on Intel’s 14-nanometer process. It supported up to about 130,000 spiking neurons and 130 million synapses. Crucially, it implemented learning rules based on spike timing - the brain-inspired idea that connections strengthen or weaken depending on the precise timing of the spikes passing through them - so the network could continue learning in place, asynchronously and without a global clock.
Loihi was a research platform, not a product. Intel distributed it to universities and labs through a research community, and used it to explore tasks where event-driven, low-power computation has an edge, such as gesture recognition, smell detection, and certain optimization and search problems. A second-generation Loihi 2 followed, and Intel later assembled many chips into large neuromorphic systems.
The significance, alongside TrueNorth and SpiNNaker, was to keep alive a distinct approach to AI hardware. While the deep-learning boom rode dense GPU arithmetic, neuromorphic chips bet that copying the brain’s sparse, spike-based, memory-near-compute style could one day deliver intelligence at a fraction of the energy.