A Leader in Neuromorphic Computing
Neuromorphic computing is a branch of artificial intelligence (AI) that simulates the functionality of the human neuron. At BrainChip, we have developed a revolutionary spiking neural network (SNN) technology, a type of neuromorphic computing that learns autonomously, evolves and associates information just like the human brain.
BrainChip’s technology has been designed as a one-shot learning system. It recognizes patterns in milliseconds without having to be pre-programmed. It achieves this by learning from information, and then later recognizing what it has learned.
BrainChip’s first application of this technology is the award-winning BrainChip Studio and BrainChip Accelerator, which aids law enforcement and intelligence organizations to rapidly search vast amounts of video footage and identify patterns or faces.
The Neuromorphic Computing Market is estimated to be worth $6.4 Billion by 2024*
*Source: Grand View Research, August 2016
How We Got Here
Peter van der Made, the visionary behind BrainChip, has dedicated the last 10 years to the company’s success. His expertise and commercial success have established him as a leader of computer innovation for the last 40 years. Peter invented one of the earliest high-resolution color graphics accelerator chips for the IBM personal computer, as well as a computer immune system that was ultimately acquired by IBM, where he was appointed Chief Scientist in 2002.
In 2015 BrainChip went public on the Australian Stock Exchange (ASX) under the ticker name BRN.
In 2016 BrainChip acquired Spikenet, a developer of custom software that utilized SNN technology in visual systems. Spikenet’s technology augmented the hardware implementation that BrainChip had been developing.
Unique within the Artificial Intelligence Sector
The AI sector includes well-known companies like Cisco Systems, IBM, Intel, Google, Microsoft, NVIDIA, Qualcomm and Samsung, which are evolving their own versions of cognitive architectures across different platforms.
BrainChip is in the unique positon of commercializing SNNs that offer instantaneous training, low computational overhead, and high accuracy in difficult environments.