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@ARTICLE{Pedretti:906936,
      author       = {Pedretti, Giacomo and Graves, Catherine E. and Van
                      Vaerenbergh, Thomas and Serebryakov, Sergey and Foltin,
                      Martin and Sheng, Xia and Mao, Ruibin and Li, Can and
                      Strachan, John Paul},
      title        = {{D}ifferentiable {C}ontent {A}ddressable {M}emory with
                      {M}emristors},
      journal      = {Advanced electronic materials},
      volume       = {8},
      number       = {8},
      issn         = {2199-160X},
      address      = {Weinheim},
      publisher    = {Wiley-VCH Verlag GmbH $\&$ Co. KG},
      reportid     = {FZJ-2022-01761},
      pages        = {2101198 -},
      year         = {2022},
      abstract     = {Memristors, Flash, and related nonvolatile analog device
                      technologies offer in-memory computing structures operating
                      in the analog domain, such as accelerating linear matrix
                      operations in array structures. These take advantage of
                      analog tunability and large dynamic range. At the other
                      side, content addressable memories (CAM) are fast digital
                      lookup tables which effectively perform nonlinear Boolean
                      logic and return a digital match/mismatch value. Recently,
                      nonvolatile analog CAMs have been presented merging analog
                      storage and analog search operations with digital
                      match/mismatch output. However, CAM blocks cannot easily be
                      inserted within a larger adaptive system due to the
                      challenges of training and learning with binary outputs.
                      Here, a missing link between analog crossbar arrays and
                      CAMs, namely a differentiable content addressable memory
                      (dCAM), is presented. Utilizing nonvolatile memories that
                      act as a “soft” memory with analog outputs, dCAM enables
                      learning and fine-tuning of the memory operation and
                      performance. Four applications are quantitatively evaluated
                      to highlight the capabilities: improved data pattern
                      storage, improved robustness to noise and variability,
                      reduced energy and latency performance, and an application
                      to solving Boolean satisfiability optimization problems. The
                      use of dCAM is envisioned as a core building block of fully
                      differentiable computing systems employing multiple types of
                      analog compute operations and memories.},
      cin          = {PGI-14},
      ddc          = {621.3},
      cid          = {I:(DE-Juel1)PGI-14-20210412},
      pnm          = {5234 - Emerging NC Architectures (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5234},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000762807700001},
      doi          = {10.1002/aelm.202101198},
      url          = {https://juser.fz-juelich.de/record/906936},
}