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wikiburner

3D processors

memristors

improved parallel processing

graphene

quantum

spintronics

optical

Did I miss any?

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Sure, you missed all the real stuff that is going on:

- II-VI channel materials on silicon

- Finfet, Nanowire FETS (the top down variant, not the academic bottom up version.)

- RRAM (Memristors are a sub-class of these devices. Memristors mainly exist in the PR department of HP)

- TSV integration

- 3D Flash

- Memory integrated processers (see Microns Automata)

It would be cool to write an article explaining this stuff and its expected impact in terms that most software developers could understand. I love reading this stuff but putting it into context would be useful. Your response suggests you know what you are talking about but it is fairly incomprehensible to most everyone else not deep into semiconductor technologies without just looking up each term in Wikipedia.

IEEE spectrum has a good and accurate general coverage of these topics.

http://spectrum.ieee.org/semiconductors

You missed the point: it will not get cheaper, because for the first time since ages they will have to make an actual effort to make a practical use of one of those fancy technologies. Also in what way would e.g. memristors or quantum computers boost the raw processing power across all types of computational problems? It's not that simple now.

Software! Every time I poke around the code of any sufficiently complex piece of software I find myself overwhelmed with the endless levels of abstraction. One benefit of plateauing hardware performance will be the slowing of the hardware churn. As innovation slows we will hopefully converge on optimal units of computation and interfaces.

As diversity decreases it will be much easier to write libraries that talk directly to the metal, protocols that write directly to the interfaces and applications that run completely isolated at native speed.

Without the towers of abstraction it is much easier to reason about the data structures of your input and output and design optimal algorithms. If we ever do come to this point there will be a renaissance in the field of computer science field.

Maybe. Much of the history of computer science is the layering of abstractions. Indeed, the transition to multi-core hasn't been the disaster that many feared because various abstractions (game engines, rendering engines, Hadoop, etc.) abstract parallelism in important ways where that parallelism is actually needed.

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