Published February 25, 2022 | Version v1.1.6
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coreylammie/MemTorch: 1.1.6 Release

Authors/Creators

  • 1. Sherbrooke University
  • 2. University of Michigan
  • 3. 3IT, UdeS
  • 4. Université de Sherbrooke

Description

Added
  1. The random_crossbar_init argument to memtorch.bh.Crossbar. If true, this is used to initialize crossbars to random device conductances in between 1/Ron and 1/Roff.
  2. CUDA_device_idx to setup.py to allow users to specify the CUDA device to use when installing MemTorch from source.
  3. Implementations of CUDA accelerated passive crossbar programming routines for the 2021 Data-Driven model.
  4. A BiBTeX entry, which can be used to cite the corresponding OSP paper.
Fixed
  1. In the getting started tutorial, Section 4.1 was a code cell. This has since been converted to a markdown cell.
  2. OOM errors encountered when modeling passive inference routines of crossbars.
Enhanced
  1. Templated quantize bindings and fixed semantic error in memtorch.bh.nonideality.FiniteConductanceStates.
  2. The memory consumption when modeling passive inference routines.
  3. The sparse factorization method used to solve sparse linear matrix systems.
  4. The naive_program routine for crossbar programming. The maximum number of crossbar programming iterations is now configurable.
  5. Updated ReadTheDocs documentation for memtorch.bh.Crossbar.
  6. Updated the version of PyTorch used to build Python wheels from 1.9.0 to 1.10.0.

Files

coreylammie/MemTorch-v1.1.6.zip

Files (6.8 MB)

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Additional details

Related works

Is supplement to
https://github.com/coreylammie/MemTorch/tree/v1.1.6 (URL)