[HN Gopher] NeuRRAM - New chip for running large-scale AI algori...
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       NeuRRAM - New chip for running large-scale AI algorithms on smaller
       devices
        
       Author : nsoonhui
       Score  : 45 points
       Date   : 2022-11-11 12:32 UTC (10 hours ago)
        
 (HTM) web link (www.quantamagazine.org)
 (TXT) w3m dump (www.quantamagazine.org)
        
       | QuadmasterXLII wrote:
       | To address a specific claim in the article: no one is doing AI
       | computation using the RAM on the motherboard lol
        
       | synthos wrote:
       | If it's so sensitive to device variance, I'd expect that it
       | doesn't behave well outside a narrow temperature range.
        
         | NextHendrix wrote:
         | It depends, with ReRAM the ion mobility in the dielectric layer
         | can be tuned, lower mobility means a higher voltage is required
         | across the cell for filament growth but a lower thermal
         | dependence.
        
           | bippingchip wrote:
           | the problem is the material properties link power and
           | variability. This means that you either get good variability
           | or higher power consumption, but never good variability at
           | low power at the same time
        
             | NextHendrix wrote:
             | That's partially true, there are other tradeoffs that can
             | be made to optimise power and thermal dependence at the
             | cost of something else but both specific use case and fab
             | availability/reliability need to be factored in.
        
         | bippingchip wrote:
         | Having worked on this for quite a few years (compute in memory
         | with a variety of emerging memories: RRAM, MRAM others) with a
         | substantial research team, our conclusion ended up being you
         | are better off with SRAM based solutions. And most likely
         | tightly coupled memory with digital compute is better than
         | doing compute in memory.
         | 
         | The newer memories like RRAM are simply not stable enough: too
         | much variations, drift with temperature, reliability etc. On
         | some cases you can try and re engineer the devices to be better
         | suited but they invariably end up being larger, or more power
         | hungry, and often both. See for example
         | https://ieeexplore.ieee.org/document/9405305 (sorry for the
         | paywall - no open access available)
         | 
         | Adding insult to injury, none of these emerging memories can be
         | integrated with highly scaled digital CMOS. (22nm is about a
         | low as you can go for eg MRAM and RRAM - where they are offered
         | as embedded flash alternatives) But you will always need
         | flexible, programmable digital compute in order to have an AI
         | accelerator that can do more than 1 flavor of resnets.
         | 
         | SRAM, in the meantime does scale relatively nicely and co
         | integrates well with digital logic across the whole spectrum
         | down to 5nm FinFETs and below.
        
           | NextHendrix wrote:
           | On the whole, at this point in time, I agree. For general
           | purpose NVM stuff you're better off going with the less
           | exotic, but SRAM isn't suitable for this specific use case.
           | Some eNVMs are essentially analogue (CBRAM, OxRAM, PCM etc)
           | whereby you can partially set a single memory cell much like
           | a variable resistor. MRAM obviously had its specific two
           | states so is unsuitable for neuromorphic computation, and
           | SRAM is the same.
           | 
           | I disagree though that 22nm is the limit for (STT) MRAM and
           | ReRAM, they both have excellent scalability.
           | 
           | SRAM scales nicely but is volatile, takes up lots of area and
           | obviously isn't BEOL compatible. You can stack MTJs between
           | metal layers just fine.
        
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       (page generated 2022-11-11 23:02 UTC)