A Technology that Learns Like the Human Brain – Neuromorphic Computing

BrainChip’s technology uses a type of neuromorphic computing called spiking neural networks (SNNs). It has many attractive characteristics, including the ability to be trained instantaneously (“one-shot learning”), high accuracy and low compute overhead. This is an important feature in the world outside of the internet, where massive datasets are not available. For instance, a police department looking for a suspect in live video streams does not have thousands of images of that suspect, nor does it have weeks to train a traditional convolutional neural network system.

What Makes Our Technology So Exciting

Our technology learns from experience, autonomously, just like a human. It does not need to be trained with millions of samples like Deep Learning, it learns a pattern instantaneously. Deep Learning networks are power-hungry and require large GPU-Server clusters and weeks of training. BrainChip’s technology learns by itself, without large datasets, and finds patterns that humans may not be aware of. This rapid learning capability opens up new possibilities to find images in video, patterns in large datasets, and hundreds of other applications where deep learning cannot be used.

Because SNNs can be implemented using regular logic functions, they are inherently high-performance and low-power.

How It Works

The neurons we have developed learn through selective reinforcement or inhibition of synapses and neuron thresholds. Learning occurs when the input is intense, or repeated through feedback at each neuron, just as the brain learns. Our fully digital neurons process input spikes in parallel, which means that the response time of the network is independent of the network size.