4 ms·

Not the original paper but this explains in detail how this works. http://www2.ece.rochester.edu/~xiguo/gomac15.pdf http://www2.ece.rochester.edu/~xiguo/gomac15

Not the original paper but this explains in detail how this works. http://www2.ece.rochester.edu/~xiguo/gomac15.pdf http://www2.ece.rochester.edu/~xiguo/gomac15.pdf I would guess they are solving PDEs using systems of linear equations. The approximate solutions to these linear equations are found via the analog linear solvers.

tanderson92 8y ago
Right, the innovation is in using storage-compute collocation to allow common data operations to be structured into the way the storage arrays are set up. The paper is really about doing sparse linear algebra in hardware not PDEs (but of course after discretization that is all PDEs are--linear algebra).
dang 8y ago
Thanks, we changed the URL to that from http://www.nature.com/articles/s41928-018-0100-6 http://www.nature.com/articles/s41928-018-0100-6 since the latter is behind a paywall without workarounds. (This is explained at https://news.ycombinator.com/newsfaq.html https://news.ycombinator.com/newsfaq.html and https://news.ycombinator.com/item?id=10178989 https://news.ycombinator.com/item?id=10178989).
- tanderson92 8y ago
  With respect (I didn't downvote you), there is a workaround: sci-hub (I checked that it is available there). For others who want a comparison; the two papers are very different: the Nature paper uses entirely analog techniques to achieve high precision results while the newly-linked article is a mixed-precision approach. That is, the newly-linked article only uses analog to obtain a low-precision estimate which is fed back to a digital computing algorithm to produce the final high-precision result. The hope is that the overall time to solution is still faster because you do not need (hopefully eventually slower) digital computation to achieve the low-accuracy result. I am not an expert in this particular hardware field (I do applied math so I am used to solving the problems they do here in digital computers), but here is what the authors of the Nature article say: > Recent theoretical and experimental studies have used memristor (including phase-change memory) arrays to generate an initial, low-precision guess (seed), and rely on an integrated high-precision digital solver to produce the required high-precision solutions from the seed solution. Such types of accelerators are certainly beneficial, as they reduce the number of iterations required by the digital solver. Determining whether memristor-based hardware can be used to directly perform high-precision computing tasks, however, will enable a better understanding of how broadly memristive hardware can be applied. Such knowledge will help pave the way to build more general memristor-based computing platforms, instead of special-purpose accelerators.
  - petermcneeley 8y ago
    I have done graduate computational physics (Multigrid and such) So im familiar with the "digital" side of this but certainly not familiar with almost any analog computations. (beyond those trivial op-amp integrators)
  - deleted 8y ago
    [deleted]
- detaro 8y ago
  FWIW, here's the university press release about the initially submitted article (although I'd guess it'd best to now ask /u/goedelmachine to submit it as a new story, this one is too confused already): https://news.umich.edu/memory-processing-unit-could-bring-memristors-to-the-masses/ https://news.umich.edu/memory-processing-unit-could-bring-me...
- msla 8y ago
  Can you mark the PDF? It crashed my browser.

tanderson92 8y ago

Right, the innovation is in using storage-compute collocation to allow common data operations to be structured into the way the storage arrays are set up. The paper is really about doing sparse linear algebra in hardware not PDEs (but of course after discretization that is all PDEs are--linear algebra).

dang 8y ago

Thanks, we changed the URL to that from http://www.nature.com/articles/s41928-018-0100-6 http://www.nature.com/articles/s41928-018-0100-6 since the latter is behind a paywall without workarounds. (This is explained at https://news.ycombinator.com/newsfaq.html https://news.ycombinator.com/newsfaq.html and https://news.ycombinator.com/item?id=10178989 https://news.ycombinator.com/item?id=10178989).

tanderson92 8y ago
With respect (I didn't downvote you), there is a workaround: sci-hub (I checked that it is available there). For others who want a comparison; the two papers are very different: the Nature paper uses entirely analog techniques to achieve high precision results while the newly-linked article is a mixed-precision approach. That is, the newly-linked article only uses analog to obtain a low-precision estimate which is fed back to a digital computing algorithm to produce the final high-precision result. The hope is that the overall time to solution is still faster because you do not need (hopefully eventually slower) digital computation to achieve the low-accuracy result. I am not an expert in this particular hardware field (I do applied math so I am used to solving the problems they do here in digital computers), but here is what the authors of the Nature article say: > Recent theoretical and experimental studies have used memristor (including phase-change memory) arrays to generate an initial, low-precision guess (seed), and rely on an integrated high-precision digital solver to produce the required high-precision solutions from the seed solution. Such types of accelerators are certainly beneficial, as they reduce the number of iterations required by the digital solver. Determining whether memristor-based hardware can be used to directly perform high-precision computing tasks, however, will enable a better understanding of how broadly memristive hardware can be applied. Such knowledge will help pave the way to build more general memristor-based computing platforms, instead of special-purpose accelerators.
- petermcneeley 8y ago
  I have done graduate computational physics (Multigrid and such) So im familiar with the "digital" side of this but certainly not familiar with almost any analog computations. (beyond those trivial op-amp integrators)
- deleted 8y ago
  [deleted]
detaro 8y ago
FWIW, here's the university press release about the initially submitted article (although I'd guess it'd best to now ask /u/goedelmachine to submit it as a new story, this one is too confused already): https://news.umich.edu/memory-processing-unit-could-bring-memristors-to-the-masses/ https://news.umich.edu/memory-processing-unit-could-bring-me...
msla 8y ago
Can you mark the PDF? It crashed my browser.