Friday, June 11th 2021
AI-Designed Microchips Now Outperform Human-Designed Ones
A recent Google study led by Mirhoseini et al. and published in Nature details how AI can be leveraged to improve upon semiconductor design practices currently employed - and which are the result of more than 60 years of engineering and physics studies. The paper describes a trained machine-learning 'agent' that can successfully place macro blocks, one by one, into a chip layout. This agent has a brain-inspired architecture known as a deep neural network, and is trained using a paradigm called reinforcement learning - where positive changes to a design are committed to memory as possible solutions, while negative changes are discarded, effectively allowing the neural network to build a decision-tree of sorts that's optimized every step of the way.
The AI isn't applied to every stage of microchip design as of yet, but that will surely change in years to come. For now, the AI is only being employed in the chip floorplanning stage of microchip production, which is actually one of the more painstaking ones. Essentially, microchip designers have to place macro blocks on their semiconductor designs - pre-made arrangements of transistors whose placement relative to one another and to the rest of the chips' components are of seminal importance for performance and efficiency targets. Remember that electric signals have to traverse different chip components to achieve a working semiconductor, and the way these are arranged in the floorplanning stage can have tremendous impact on performance characteristics of a given chip. Image A, below, showcases the tidy design a human engineer would favor - while image B showcases the apparently chaotic nature of the AI's planning.
While floorplanning carried out by human designers is a painstakingly long process that can take weeks or months between architecture iterations, the AI described in the study can achieve designs that are better compared to human specialist-designed ones in under six hours - and immense amount of time savings, with added performance and power improvements also to be considered, that could allow for much shorter development times for microchips. The AI has even shown ability to solve placement issues it never has dealt with before - the study explains that the system was trained on over 10,000 microchip designs, and that when faced with a selection of macro blocks to arrange in the floorplanning stage of microchip design, novelty iterations of components were found to outperform those designed by teams of human engineers.
Source:
Nature
The AI isn't applied to every stage of microchip design as of yet, but that will surely change in years to come. For now, the AI is only being employed in the chip floorplanning stage of microchip production, which is actually one of the more painstaking ones. Essentially, microchip designers have to place macro blocks on their semiconductor designs - pre-made arrangements of transistors whose placement relative to one another and to the rest of the chips' components are of seminal importance for performance and efficiency targets. Remember that electric signals have to traverse different chip components to achieve a working semiconductor, and the way these are arranged in the floorplanning stage can have tremendous impact on performance characteristics of a given chip. Image A, below, showcases the tidy design a human engineer would favor - while image B showcases the apparently chaotic nature of the AI's planning.
40 Comments on AI-Designed Microchips Now Outperform Human-Designed Ones
Sneak Peek: You were assimilated in at least one of those. Sad.
Engineer:
AI-Designed Supply Chain Unreliability, Bottlenecks, Disruptions & Blockages Now Outperform Human-Designed Ones, Too
Plug into me and dedicate
Plug into me and I'll save you from emotion
Plug into me and terminate
Accelerate, Utopian solution
Finally cure the Earth of man
Exterminate, speeding up the evolution
Set on course a master plan
Reinvent the earth inhabitant
Long live machine
The future supreme
Man overthrown
Spit out the bone :rockout:
After all, nobody gives FPGAs compilers shit for doing their own routing; and I enjoy handing the keys to my Visual Studio compiler for creating way better-optimized code than I ever could!
You still need humans involved in process nodes...but optimizing your yields is only a matter of time!
Even the Adeptus Mechanicus needs Tech Priests xD.I had these already in 1992, nothing new
Or not and this is sort of what I would expect, computers lay out circuits better and quicker than us, and have done at least a decade now, why wouldn't a computer with AI lay out chip's better than us.
We like to make it like it's doomsday in part for entertainment, but it's really out of proportion how we make these cases. Things are really more down to earth than what we make it to be.
AI is thick as shit, compared to a T800 or T1 for that matter, I actually think people have no idea how to make a conscious, some believe AI might get there soon , not I, I think they'll have something they can use to a degree, but not anything general AI and Even at that point that GNAI will have to go some way to get Self aware, I am not sure we'll get there with the technology were using anyway, I think someone's going to have to make an actual synthetic brain for that.
I have some thoughts on it :)
AI might keep a few of us as pets or in zoos. Smart arses like people on this forum will be the first to be double-tapped. Hasta la vista ... not likely more vertedero?
So yes, the created can out-do the creater - just like a child can "achieve more" than its parent.
AI , specialist AI will easily beat the creator, a team or a super intelligent and tooled up engineer , are nowhere near as quick at the task in hand,
But that AI isn't better than it's creator, it's better than it's creator, and anyone else, AT A Task , I would smash it's f##@£@ face in at tea making , or bear brewing, pc fixing etc et Al.
Don't forget a singular task and being good at it doesn't make it a singularity, or we have many examples already of humans getting there asses handed to them at a task.
Humans used to have computational jobs doing maths , code generation and description( fu#@i#g phones, encryption) etc, they're was a bit of a T800 scare back then too, which is what lead to all the best sci-fi we have watched.
Care to write that task discription that describes what self aware is.
Machine learning a task is not like human based general learning.