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Forty Years of Moore's Law
kjh1 writes "CNET is running a great article on how the past 40 years of integrated chip design and growth has followed [Gordon] Moore's law. The article also discusses how long Moore's law may remain pertinent, as well as new technologies like carbon nanotube transistors, silicon nanowire transistors, molecular crossbars, phase change materials and spintronics. My favorite data point has to be this: in 1965, chips contained about 60 distinct devices; Intel's latest Itanium chip has 1.7 billion transistors!"
It's not a law, it's an observation. Did you know the term 'law' for a scientific theory was coined by Isaac Newton, who felt that his 'Laws of Motion' were so right and pervaded the universe so deeply that they had to be a law? He wanted to convey they had a deeper significance than a mere theory. In time of course, even these 'laws' came to be shown to be incomplete or only true for slow moving objects. Ever since, every theory both worthy and crackpot has been called a 'law'. It's about time we returned to the humbler 'theory', 'theorem' or 'observation'. In the case of Moore's 'Law', it's not even a very good theory, since it only describes a very general trend, it cannot predict with any accuracy exactly how fast/how many transistors or elements a chip will have at any time in the future.
By the way, if the Itanium has 1.7 billion transistors, (I'll take the poster's word for it) then one has to ask - are they all pulling their weight? It seems a hell of a lot for what it does. Surely one way to squeeze more out of Moore's Observation is to come up with more efficient architectures and use fewer devices, working more efficiently/smarter/harder. Just a thought.
...the moment. It depends on your application of course. But for number crunching it's hard to beat the GPU on recent graphics cards. For non-graphics applications you can expect speedups from 5-15 times (not %) for things like linear algebra, option pricing and singnal processing. This has been increasing faster than Moore's Law and will likely increase faster. Code written for GPUs is inherently streaming code, and hence easily parallelisable, so many of the complex dependencies that make CPUs tricky to speed up go away. These are exciting times and a big shift in programming paradigm is taking place.
Doesn't it make you feel good to know that our freedoms are protected by politicans, lawyers and journalists.
Well, several reasons come to mind:
- Software usually performs a more diverse set of options
- The environment where hardware runs is more predictable than the software one
- Formal verification is probably easier to perform with hardware.
- It's easier to verify low level stuff than high level abstractions.
I'd add more, but I've got other things to do unfortunately...
The AACS key is NOT 0xF606EEFD628B1CA427BEA93A9CA9773F
Actually, you're wrong in assuming his law will cease to apply once it becomes impossible to make transistors, as the law didn't apply specifically to transistors in the first place.
His observation was made to Electronics magazine, in the April 19th, 1965 edition.
He didn't mention transistor density.
He didn't mention processors (as microprocessors were still 6 years away from being invented).
He was describing component integration on economical integrated circuits.
He observed that component integration doubled approximately every 12 months. He increased that number to 24 months, in 1975. Since then, other people have split the difference to 18 months.
None of those figures, 12, 18 or 24 months, are accurate.
If the 18 month figure was accurate, today's chips would have 75 Billion transistors.
With his original 12 month figure, 27 Trillion.
With his revised 24 month figure, 37 Million...
Also, this isn't even a law... it's an observation.
Please note... I relied on Tom R. Halfhill's column in Maximum PC (April 2005) "The Myths of Moore's Law" for this reply.
-- This sig for rent.
If it actually works, then there's little to complain about.
It can only work for so long. The biggest problem that is keeping performance down is not the processor but the memory retrieval and writing system: only one memory location can be accessed at any one time. This is also known as the von Neumann bottleneck. Not even clustering can get around this problem because there is a need for inter-process communication that slows things down. If someone could come up with a system that allows unlimited random and simultaneous memory access, the physical limit to processor speed would not be such a big deal anymore. We would have found the holy grail of fast computing.