On the way to a human-like artificial intelligence, it is certainly not wrong to learn from nature. To imitate nature and their principles: bionics. Researches in Israel did it and created the ‘Neuro-Chip‘. Read more on ‘VonNaftali’: Subscription $29,90/12 months.
The Technion deployed this bionic approach and it seems to yield results. The solution lies within the architecture of the chip. The Technion created an ‘Neuro-Chip’. The release explains: “Since they became part of our lives some 80 years ago, computers have become faster and smaller, but their basic architecture hasn’t changed.
There’s still one part that stores information – that’s the memory (e.g., RAM, hard drive), and another part that processes information – that’s the CPU or processor. Now Associate Professor Shahar Kvatinsky presents an architectural alternative.
Bringing the “thinking” and the “remembering” functionalities together into one unit, he has built a neural network right into the hardware of a chip, and as a proof of concept – taught it to recognize handwritten letters. The results of his study were recently been published in Nature Electronics.”
Neuromorphic Network, GO! and $3.000 Electricity Bill
“We like to describe a computer as a ‘brain’, but entirely separate hardware for storing information and for using it is not how an organic brain works,” explains Prof. Kvatinsky. He developed neuromorphic hardware. Means, electronic circuits inspired by neuro-biological architectures as present in the nervous system.
One might think modern computers are already surpassing the human brain – has not a computer already defeated the best human chess and Go players? Although the answer is “yes,” AlphaGo, the program that defeated multiple Go masters, relied on 1500 processors, and accrued a $3000 electricity bill per game.
The human players’ energy consumption for the same game amounted to a sandwich, more or less, and that same player is also capable of talking, driving, and performing countless other functionalities. Computers still have a long way to go.
Artificial neural networks learn in a way similar to living brains: they are presented with examples (examples of handwritten letters, in this particular study), and “figure out” on their own the elements that make one letter different from others, but similar to the same letter in different handwriting.
When the neural network is implemented as hardware, the learning process strengthens the conductivity of some nodes. This is very similar to how, when we learn, the connections between neurons in our brains are strengthened, explains the Technion.
‘Neuro-Chip‘ saves Energy
The ‘Neuro-Chip‘ does everything like an organic brain: stores the information and processes it. This chip is also hardware-only, meaning its programming isn’t separate; it is integrated into the chip. What this chip does is learn; specifically, learning handwriting recognition, a feat achieved through deep-belief algorithms.
Unlike most of the ‘neuromorphic chips ‘Neuro-Chips‘ investigated these days, that use emerging unconventional technologies, this chip is based on commercial technology available in Tower Semiconductor foundries. Presented with multiple handwritten examples of each letter, the chip learnt which one is which, and achieved 97% accuracy in recognition with extremely low energy consumption.
That is without any doubt a huge steps forward. On the horizon are server farms with a energy consumption reduced by a factor 10 or more. So, it is better to put the brain into the chip and not a chip into the brain.
Based on pressrelease by Technion, selected as relevant, shortend, regrouped and adapted by VonNaftali. The word ‘Neuro-Chip’ was coined by VonNaftali.
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