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MIT Develops New Chip That Reduces Neural Networks' Power Consumption by Up to 95 Percent (mit.edu)
MIT researchers have developed a special-purpose chip that increases the speed of neural-network computations by three to seven times over its predecessors, while reducing power consumption 94 to 95 percent. From a report: That could make it practical to run neural networks locally on smartphones or even to embed them in household appliances. "The general processor model is that there is a memory in some part of the chip, and there is a processor in another part of the chip, and you move the data back and forth between them when you do these computations," says Avishek Biswas, an MIT graduate student in electrical engineering and computer science, who led the new chip's development. "Since these machine-learning algorithms need so many computations, this transferring back and forth of data is the dominant portion of the energy consumption. But the computation these algorithms do can be simplified to one specific operation, called the dot product. Our approach was, can we implement this dot-product functionality inside the memory so that you don't need to transfer this data back and forth?"
The tensor processing units Google developed seem also very capable compared to regular processors. Does anyone know how MIT's new chips stack up against what Google already has in operation?
Just imagine a Beowulf cluster of these things ;)
"The general processor model is that there is a memory in some part of the chip, and there is a processor in another part of the chip, and you move the data back and forth between them when you do these computations"
Registers and L1 cache? Wow, that's new!
mine cryptocurrencies?
Some new ranking for chips in terms of minability.
Domestic spying is now "Benign Information Gathering"
Call Michael Biehn immediately. We need to resolve this situation ....
Looking at what is available today, I would have to say that today's smart world is incredibly stupid. Not to mention fractured with loads of standards, apps and do-dads.
When Google took over Nest I had high hopes.
I had imagined they would do something clever like install their phased array mics into the "smart" fire alarms that could be in almost every room. Then from anywhere you could ask google something. But no... you need to find some stupid crappy little speaker and keep shouting HEY, GOOGLE, HEY GOOGLE, HEY GOOGLE, until it finally can hear you.
With these chips, they could take that idea even further. Install connect appliances, connected switches and sockets and then figure out the patterns of usage and voices to "learn your ways" and begin to antisipate thing.
Oh.. Bob always turns on the TV right after he grabs a beer from the fridge around 6pm. The fridge just opened, so I will turn on the TV for him.. also it was cold today, so i will adjust the heat in that room so Bob's ass doesnt get to cold on his leather lazy-boy.
I should think that is all totally possible today.
Since video cards have specialized processors that handle dot products (and all sorts of other matrix computations) like mad, how is what they are proposing much better than existing GPU's? In particular it seems like nVidia has been doing a ton of work to tailoring GPU's to be used with neural networks.
"There is more worth loving than we have strength to love." - Brian Jay Stanley
The major vendors aren't nearly as interested in dropping the system hardware cost as they are in having plausible access to live microphone streams. Since the user is the product, and privacy is irrelevant, its now all about the data mining for advertising and related behavioral research. This also keeps the IP in the neural networks away from competitors and open source developers prying eyes. These chips might be used for some preprocessing, but these vendors want that data stream to continue as long as possible...
And the miracle solution is...? Don't use any of them. See how easy that was. You all are like a drug addict complaining about his addiction.
Interesting development...
But my understanding of this whole deal, and I might be wrong, is that we already have more than enough to make AIs local... this isn't a problem of capability, this is companies behind AI assistants trying to harvest as much data as possible from their costumers and turn a profit from it, and/or to use it for themselves.
So... how good is it at computing SHA256 hashes? ;)
Anons need not reply. Questions end with a question mark.
That sounds like something an FPGA could do from the very beginning.
The only new thing here would be possibly LARGER amounts of memory stored inbetween the fabric (reducing off-chip access, and increased number of LUTs not tied up as memory cells), and possibly like they said, combined "access and modify" operations.
But I think the article itself doesn't understand what it's talking about then.
And as general purpose as FPGA are in idea, they "custom adapted" to different tasks (and layout/fabric) since inception. So the question here is, are they talking about some kind of ASIC advancement that they didn't have before?
>The chip can thus calculate dot products for multiple nodes — 16 at a time, in the prototype — in a single step, instead of shuttling between a processor and memory for every computation.
This appears to be the only actual advancement/tech/change, being extruded out into an entire fluff article for college PR purposes.
Personally, I'm way more interested in getting my hands on an "FPGA in CPU" ever since back in college when Altera was bought by Intel. Imagine a CPU that can be told to add CUDA cores when you start a game, or SHA cores when you start a server. Altera specializes is live reconfigurable FPGAs. FPGA's that can be "flashed" in whole or in part while still running.
Neural network in my toaster...awesome! When will I be able to buy one?
Such things include "Computational Ram"
https://en.wikipedia.org/wiki/...
There is also a very old idea of using memory elements directly to compute results, which is true memputing. (There are few examples of this, because it is costly as an architecture-- but your brain is a pretty good biological example. The same components are used for data storage, as well as data processing.)
Given that such "Computational Ram" devices already exist in the wild, I fail to see why more novel hardware is needed, excepting as a refinement of concept?
But can it mine cryptocurrencies?
A better question is "could a breakthrough in quantum computing make mining cryptocurrencies trivial?" (Making the Dutch Tulip market collapse look like a minor market correction."
All this talk of moving things back and fourth reminds me of Middle Out Compression...
That could make it practical to run neural networks locally on smartphones
I thought EVERY smartphone had a neural network chip in it already: that's how modern auto-focus works, unless I was misinformed.
so does this mean I can run this chip with a double A battery?
Isn't this the chip the reverse engineered from that bit of waste found in the steel plant after the Terminator was destroyed?
Oh just great! Looks like Iâ(TM)ll soon have to replace all my old appliances which employ âoefuzzy logicâ with nee appliances that employ neural networks.
ARM processor + FPGA fabric?
And of course, the Virtex series has had (not incredibly well supported) partial reconfiguration on the fly for at least 10 years, and you can instantiate a CPU core of your choice.