>> It seems that a large portion of the savings come from being application specific and using analog computation.
Yes it is analog computation but a different beast of analog computation. You are looking at weak inversion biasing of transistors which gives you low power but you have to deal with lots of noise issues in your design.
>> But since Google has already simulated a 10 billion simplified neuron system on only 16 GPU's and used it fr computer vision one has to wonder whether simplified neurons will do
Google did that using FPGA and massive computing power which adds not a lot of value when compared to the real brain. We are not talking of having a datacenter to power your brain. We want something less power hungry and efficient that is close to the natural one. That is the innovation in Kwabena's work.
>> 100,000 times more energy efficient, amazing...congrats Kwabena! maybe it is time for you to give a talk at emerging technologies conference
Kwabena is such a big fella to get invitation this way. He is truly leading the neuromorphic nexus. The path to human immortality could start this small as after cloning the brain in a circuit, the next phase will be making it to replace the one we have in case one needs a better one.
The relatively low power consumption is not quite as incredible as it sounds. It seems that a large portion of the savings come from being application specific and using analog computation.
That is not meant to diminish the achievement. There seems to be significant potential use for analog (and other approximate) computation and special purpose design. Not only could the Neurogrid device be useful in studying biological systems, but the project's work on hardware and software design might help advance use of similar systems for other areas.
This is definetly asn interesting project, and it will surely be useful for learning more about the brain.
But since Google has already simulated a 10 billion simplified neuron system on only 16 GPU's and used it fr computer vision one has to wonder whether simplified neurons will do , or we really need complex neurons for engineering applications ?
My Mom the Radio Star Max MaxfieldPost a comment I've said it before and I'll say it again -- it's a funny old world when you come to think about it. Last Friday lunchtime, for example, I received an email from Tim Levell, the editor for ...
A Book For All Reasons Bernard Cole1 Comment Robert Oshana's recent book "Software Engineering for Embedded Systems (Newnes/Elsevier)," written and edited with Mark Kraeling, is a 'book for all reasons.' At almost 1,200 pages, it ...