This repo contains the code for the Digital Twin (DT) used in Microsoft Research's AOC project.
Paper: $PAPER
The DT provided in this repo can be used to train fixed-point models to run on either the newer 2300-weight or the 256-weight AOC machine that is published in the journal paper.
At its core, AOC implements the following idealized iteration
z_t+1 = alpha z_t + beta W tanh(z_t) + x + b,
which allows it to rapidly evaluate fixed-point models such as Deep-Equilibrium (DEQ) or Hopfield models. The input to the model x is reinjected at every iteration in line with the formulation of these models.
The output is then defined as the fixed-point z*.
However, various non-idealities perturb the computation in the AOC device away from the equation above, creating the need for a faithful digital twin through which the models can be trained digitally so that they can be evaluated correctly on AOC.
The package can be installed via pip install aoc. It requires python>=3.10 and a torch>=2.0 as well as torchdeq,
einops, pyyaml, and pytest. Consult the pyproject.toml for details.
The core of the digital twin is the AOCCell class which attempts to model the physics of the analog-optical device.
AOCCell needs to be wrapped by initial and final layers ensuring correct input and output dimensionality.
We provide a ready-made wrapper that offers linear input and output projections with the DEQInputOutputProjection class.
We recommend instantiating this class via its DEQInputOutputProjection.create_default_aoc_model(...) factory method.
AOCCell supports a variety of structured matrices. The simplest matrix structure is dense and can be achieved by passing
d_hidden=[s, s] where s is the desired size of the matrix. If a multi-layer structure should be simulated, more
intermediate sizes can be provided, for example, d_hidden=[24, 24, 24] creates a two-layer structure which would still
fit the 2300-weight machine.
By default, a DEQ-structure is created which means that all layers are laid out below the diagonal with a single
feedback block on the top right. For the d_hidden=[24, 24, 24] configuration, this would mean that the matrix consists
of two 24x24-shaped blocks along the antidiagonal.
The MatrixConnectivityType argument controls the type of the matrix and can be set to
Feedback(i.e. DEQ).Feedforward, which means that no feedback occurs through the matrix.Hopfield, which ensures a symmetric matrix.
We also provide a more idealized implementation of the hardware operation in the SimpleCell class.
We recommend the constructor wrapper-model factory method DEQInputOutputProjection.create_simple_cell_model(...)
to experiment with this cell type.
The exact hardware configuration is controlled by the abstract HardwareParameters class which comes with two child classes:
HardwareParameters16 and HardwareParameters48 corresponding to the previous and the current AOC hardware generation.
We provide a yaml configuration for each hardware generation in src/aoc/hw_config.
AOCCell itself offers further options to turn on or off a range of non-idealities. Each non-ideality is attempts to
empirically model a particular effect in the hardware that is relevant enough to impact the result of the analog computation.
For details on these effects, please consult the paper supplementary section D.
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