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Neural technology advances with AVFE system

Posted: 04 Mar 2016     Print Version  Bookmark and Share

Keywords:neural network processor  data transfer  visual data 

BrainChip Holdings Ltd. (Perth, Australia) has recently developed an Autonomous Visual Feature Extraction system (AVFE) based on its spiking neural processor technology.

SNAP, BrainChip's neural network processor uses signal spikes for data transfer. The method is known as Spike Time Dependent Plasticity (STDP) for learning.

The AVFE is a breakthrough in that has demonstrated unsupervised learning from a visual data stream with implications for applications such as collision avoidance in autonomous driving and drones.

 AVFE

Figure 1: SNAP uses signal spikes as a means of data transfer and a method called Spike Time Dependent Plasticity (STDP).

The AVFE on SNAP is able to process 100 million visual events per second. And within seconds learns and identifies patterns in the image stream, BrainChip said in a regulatory statement for the Australian Stock Exchange. The AVFE/SNAP was attached to a Davis artificial retina purchased from the developer Inilabs GmbH (Zurich, Switzerland) as a source of streaming digital video information.

The Davis Dynamic Vision Sensor is an artificial retina that has an AER (Address Event Representation) interface, the same interface that is used by SNAP. Rather than outputting frames of video, each pixel outputs one or more spikes whenever the contrast changes.

Potential applications for the AVFE running on SNAP and linked to an appropriate source include collision avoidance systems in road vehicles and drones, anomaly detection, surveillance and medical imaging.

The system initially has no knowledge of the contents of an input stream. The system learns autonomously by repetition and intensity, and starts to find patterns in the image stream. This image stream can originate from a visible image sensor – such as Davis – but alternatively from an appropriately engineered radar or ultrasound source.

The AVFE was tested on a highway in Pasadena, California, in a trial run lasting 78.5 seconds. The SNAP spiking neural network learned to recognise cars and started counting them in real time.

captured visual image

Figure 2: Visual image captured using Inilabs' Davis silicon retina showing cars travelling along highway.

(Source: BrainChip Inc.)

Peter van der Made, BrainChip CEO and Inventor of the SNAP neural processor said: "We are very excited about this significant advancement. It shows that BrainChips neural processor SNAP acquires information and learns without human supervision from visual input."

The development of AVFE has prompted BrainChip to expand its commercial efforts and form a partnership with Applied Brain Research Inc. (Waterloo, Ontario). The two companies have entered into joint development and marketing agreement.

- Peter Clarke,
  EE Times





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