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Computational memory can't speed up 'everything' because most software doesn't benefit from single operations that span a large chunk of data (AKA SIMD ops, as
by randcraw 9y ago
Computational memory can't speed up 'everything' because most software doesn't benefit from single operations that span a large chunk of data (AKA SIMD ops, as on GPUs). This technique will always improve only a small select fraction of present and future software, like games/graphics, video and voice processing, and general purpose vector operations (like tensor ops in deep learning).
- marcosdumay 9y agoOr programs are not written to use computational memory because there isn't any for them to use. Besides, computational memory is not restricted to SIMD. It's limited into local memory, but not at synchronized execution.
- Dylan16807 9y agoHow many programs can even manage to use two full-featured cores in parallel, with everything made as easy as possible? How many even try to do calculations on a GPU? I'm not holding my breath for more than 1% of programs to use this. Or if it gets built into GPUs, then 1% when possibly excluding rendering code.
- adrianN 9y agoHow many programs need to? The ten year old laptop I'm writing this comment on is fast enough for everything I want to do. Even my Raspberry Pi is fast enough for almost everything I want to do.
- otakucode 9y agoThere is no reason whatsoever that this would be limited to SIMD operations. They will be easy to do because you just spam a certain set of gates out in an array, but you could be laying down completely novel, or programatically determined, gates instead.