[HN Gopher] Magnetic tunnel junction-based computational random-...
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       Magnetic tunnel junction-based computational random-access memory
        
       Author : PaulHoule
       Score  : 27 points
       Date   : 2024-01-03 17:11 UTC (5 hours ago)
        
 (HTM) web link (arxiv.org)
 (TXT) w3m dump (arxiv.org)
        
       | hinkley wrote:
       | I first heard of computational RAM some time around 1999-2001
       | [edit: in the context of parallel rendering]. Looking through the
       | bibliography here most of their computational citations are from
       | 2011-2017, which confused me at first.
       | 
       | I think the deal with this paper is not what, but how.
       | In this work, a CRAM array based on magnetic tunnel junctions
       | (MTJs) is experimentally demonstrated.         First, basic
       | memory operations as well as 2-, 3-, and 5-input logic operations
       | are studied. Then, a         1-bit full adder with two different
       | designs is demonstrated. Based on the experimental results, a
       | suite of modeling has been developed to characterize the accuracy
       | of CRAM computation. Further          analysis of scalar
       | addition, multiplication, and matrix multiplication shows
       | promising results.
       | 
       | So it's a funky kind of memory cell. Which they then...
       | These results are then applied to a complete application: a
       | neural network based handwritten digit classifier,
       | 
       | So we have again arrived at the point in the R & D epicycle where
       | every research paper is linked to the buzzword of the day to
       | attract the very short attention spans of investors.
        
         | PaulHoule wrote:
         | No, CRAM has been of interest for neural networks for a long,
         | long time. I find the "buzzword" analysis to be infuriating
         | because people have been developing neural systems for decades:
         | as annoying as those NIST digits (a facile response to so many
         | papers is: "has anyone ever asked if it would work for some
         | other data set?") but Yan LeCunn delivered a handwritten digit
         | recognizer to the US post office in the early 90s, I worked on
         | a neural search engine for patents 10 years ago, etc.
         | 
         | If you are annoyed by the people who pivoted from NFTs to LLMs,
         | I am annoyed even more.
        
           | hinkley wrote:
           | CRAM is not limited to AI. I first saw it demonstrated as a
           | distributed 3D graphics engine (though at the time it was
           | more like vector graphics) where each chip was responsible
           | for a different section of the display. Closer to ray tracing
           | than neural networks.
        
             | PaulHoule wrote:
             | ... and lots of other things too. One early application of
             | content-addressable memory was STARAN
             | 
             | https://en.wikipedia.org/wiki/STARAN
             | 
             | which was used in air traffic control systems to project
             | the trajectories of all the aircraft being tracked and test
             | if any are at risk of collision.
        
       | tempodox wrote:
       | What is Computational RAM (CRAM)?
       | 
       | > CRAM performs logic operations directly using the memory cells
       | themselves, without having the data ever leave the memory.
       | 
       | Sounds like an interesting new approach to computation if we can
       | make this work at scale.
        
         | hoten wrote:
         | Does that mean no registers? Is there still a need for L1/2/3
         | caches?
        
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