Why Intel built a neuromorphic chip

Intel has made several acquisitions and revamped its roadmap for deep learning. The new portfolio extends from Knights Mill and Lake Crest (Nervana) for training neural networks to Xeons, Altera FPGAs and Movidius vision processors for running these models.

Now Intel has added another chip to the mix with the announcement of Loihi. This one, however, is a bit different. For starters, it isn’t part of the company‘s AI Products Group, but rather Intel Labs, which spent about six years developing the test chip. In addition, Loihi has a completely different, “self-learning,” neuromorphic architecture with the potential to tackle a broader class of AI problems.

The concept of a computer that mimics the brain isn’t new–Caltech scientist Carver Mead began working on it in the 1980s and coined the term “neuromorphic”–but these have largely remained science projects with little commercial application. In an interview, Narayan Srinavasa, Senior Principal Engineer and Chief Scientist at Intel Labs, explained why the company chose to go down this path.

Moore’s Law scaling has allowed Intel to pack a lot more cores in a given area. (This week Intel announced its first mainstream desktop chips with six cores and began shipping the Core i9 chips with up to 18 cores.) But the truth is that many workloads can’t exploit all those cores, Srinavasa said, which has led to a phenomenon known as dark silicon. In other words, it simply isn’t efficient to light up all those transistors all the time. To address this, the industry needs both a more efficient architecture and complementary workloads that can take advantage of all these cores.

Intel and others have been inspired by the design of the brain because it is extremely efficient at what it does. The human brain has an estimated 100 billion neurons each with up 10,000 synaptic connections–or a total of some one quadrillion synapses–yet operates on less power than a lightbulb. Of course, neuromorphic chips can’t close to this scale yet. The 14nm Loihi test chip is organized into 128 clusters, each containing 1,024 neurons, for a total of around 130,000 neurons with 130 million synapses distributed across them.

But the chip operates on similar principles–at least to the extent that we understand how the brain works. When the pulses or ‘spikes’ sent to a neuron reach a certain activation level, it sends a signal over the synapses to other neurons. Much of the action, however, happens in the synapses, which are ‘plastic,’ meaning that they can learn from these changes and store this new information. Unlike a conventional system with separate compute and memory, neuromorphic chips have lots of memory (in this case SRAM caches) located very close to the compute engines.

There is no global clock in these spiking neural networks–the neurons only fire when they have reached an activation level. The rest of the time they remain dark. This asynchronous operation is what makes neuromorphic chips so much more energy efficient than a CPU or GPU, which is “always on.