2020 BRN Discussion, page-10960

This article has been updated again...

Conventional computer systems process data completely differently to biological brains in a way which can only support useful AI through brute force i.e. high powered, big computers.

Neuromorphic processing is based on observing that the human brain supports massive 'actual' Intelligence in a small space and at very modest power consumption. The approach is to mimic the neuronal synaptic architecture and organic self-organisation of the human brain.

Easier said than done, and Brainchip has been working on it for nearly two decades.

The ability to capture masses of data, discern the salient features of that data and later to instantaneously recognise those features – is central to high speed decision making. You can sum it up in one phrase “I’ve seen this before”.

"Salient features" is the operative term in the above paragraph. This introduces the concept of Spike Neural Processing and from where the name "Akida" derives. Akida is Greek for spike. Whereas brute force AI (using traditional Von Neumann architecture) must process all data looking for patterns, brains cut the workload down through spiking events where a neuron reaches a set threshold in response to new sensor data before transmitting a signal to a cascading sequence of subsequent neurons (a neural network).

The difference between the two architectures can be illustrated using fitting geometric shaped blocks into a board as an analogy...

• Traditional (Von Neuman) computing works sequentially – try every shape one at a time in the remaining holes until they are all fitted.

• Spike Neural processing – pick them all up at once and insert them at the same time – parallel processing.

Shape boards are used by cognitive psychologists to measure brain development in children. At a certain age (usually by 2 years) a child switches from trial and error to recognising which shapes fit in which holes. They only fit them one at a time because it's physically difficult to handle them all at once.

Thus both human brains and Akida™ only process the spike events, and process them in parallel (simultaneously) which dramatically cuts the workload, speeds response time dramatically, and reduces power consumption.

Learning is about setting and fine tuning spike thresholds and setting-up a pattern of links to other neurons. This is how the brain operates and Akida™ mimics this structure in the nueromorphic fabric at the core of the chip.

Extract fromhttps://jwpm.com.au/industrial-marketing-blog/brn-brainchip-neuromorphic