# PIMCOMP paper models

This directory contains the four networks evaluated in
[PIMCOMP: An End-to-End DNN Compiler for Processing-In-Memory Accelerators](https://arxiv.org/pdf/2411.09159):
VGG-8, ResNet-18, ResNet-34, and GoogLeNet.

See the runner-generated [results.csv](results.csv) for the current latency
and energy results.

## Models and provenance

| Directory | Model | Input | Provenance |
| --- | --- | --- | --- |
| `resnet18/` | ResNet-18 v1 | `1x3x224x224` | Symlink to the complete ONNX Model Zoo model already present at `../resnetv2/depth_68/resnetv2_depth_68.onnx`. |
| `resnet34/` | ResNet-34 v1 | `1x3x224x224` | [ONNX Model Zoo `resnet34-v1-7`](https://huggingface.co/onnxmodelzoo/resnet34-v1-7), with its symbolic batch fixed to 1 as PIMCOMP's frontend does. |
| `googlenet/` | GoogLeNet | `1x3x224x224` | Unmodified [ONNX Model Zoo `googlenet-12`](https://huggingface.co/onnxmodelzoo/googlenet-12). |
| `vgg8/` | VGG-8 reconstruction | `1x1x28x28` | Deterministic compiler workload with six convolution and two fully connected layers. |

`googlenet/googlenet-12-latency.onnx` is the explicit common latency model.
It exposes the original model's final FC logits (`loss3/classifier_1`) and
removes its two LRN nodes and terminal Softmax so the comparison covers only
operations scheduled by PIMCOMP. Keep `googlenet-12.onnx` as the unmodified
source model.

The PIMCOMP authors did not publish the ONNX checkpoints used by the paper.
Running PIMCOMP's frontend on the three Model Zoo files above produces JSON
graphs exactly equal to PIMCOMP-NN's bundled `resnet18.json`, `resnet34.json`,
and `googlenet.json`.

There is no VGG-8 artifact in the ONNX Model Zoo or any PIMCOMP-NN revision.
The included VGG-8 therefore has deterministic random weights and is suitable
for compiler and simulator comparison, not paper-accuracy reproduction. The
paper also says that VGG-8 and ResNet-18 were trained on MNIST, while the
published PIMCOMP graphs and ResNet Model Zoo artifacts use ImageNet shapes.

Current SHA-256 checksums:

```text
788088b908e233d924c7c26b997e89ee861290c7bc56783a306e8201d79aac8f  resnet18/resnet18-v1-7.onnx
c3231061d081bdd47884137b02134f85142752a39e87263c529cd14ed242b096  resnet34/resnet34-v1-7.onnx
c99c507058eaf41de8723408fdda7db8325cb57f0a89f2ee07a716d6e963e14e  googlenet/googlenet-12.onnx
a26f9e33901c573e60c34a3f0abbb4744fff83e4e0f21b18fc66e20395e72982  googlenet/googlenet-12-latency.onnx
396cdea21e5e7d02c3f26f14d22ef20975171702493f5c5e79b8e0d896e541ef  vgg8/vgg8-mnist-reconstructed.onnx
```

## Paper hardware profiles

The files in
[`../../pimsim_configs/pimcomp/`](../../pimsim_configs/pimcomp/)
encode Table V's explicit resource parameters.
Each profile subdirectory contains pre-generated latency and throughput
`pimsim-nn` configs plus its matching mesh; comparison and validation select
these checked-in artifacts without generating configs at runtime.

| Config | Cores | Crossbars/core | Crossbar | Cell | PIMCOMP layout |
| --- | ---: | ---: | --- | ---: | --- |
| `arch-a/latency_config.json` | 168 | 96 | `128x128` | 2-bit | `12x14` |
| `arch-b/latency_config.json` | 138 | 128 | `128x128` | 2-bit | `6x23` |
| `arch-c/latency_config.json` | 64 (16 chips x 4) | 8 | `512x1024` | 2-bit | flattened `8x8` |

`adc_count` is 16, matching the paper's 16-bit fixed-point weight precision.
The paper does not give a two-dimensional core topology for Arch-A/B, so the
factorizations above preserve core count but cannot reproduce unpublished NoC
placement details. Released PIMCOMP-NN has no chip-count field; Arch-C is
therefore flattened to 64 cores and does not model chip boundaries.

The remaining latency and power values come from PIMCOMP-NN's released default
configuration. Consequently, instruction/resource comparisons are
reproducible, but absolute paper power and energy numbers are not.

## Build and validate the ONNX files

From the Raptor repository root:

```bash
.venv/bin/python -m pip install -r requirements.txt
cmake --build ./build_release
cmake --build third_party/PIMCOMP-NN/build --target PIMCOMP-NN

.venv/bin/python -c \
  'from pathlib import Path; import onnx; [onnx.checker.check_model(onnx.load(p)) for p in Path("validation/networks/pimcomp_models").glob("*/*.onnx")]'
```

Do not build either project with `ninja` directly.

## Compile with PIMCOMP

PIMCOMP-NN reads `third_party/PIMCOMP-NN/config.json` directly. Back it up,
select one paper profile, and restore it when the shell exits:

```bash
RAPTOR_ROOT=$PWD
PIMCOMP="$RAPTOR_ROOT/third_party/PIMCOMP-NN"
PIMCOMP_CONFIGS="$RAPTOR_ROOT/validation/pimsim_configs/pimcomp"
CONFIG_BACKUP=$(mktemp)
cp "$PIMCOMP/config.json" "$CONFIG_BACKUP"
trap 'cp "$CONFIG_BACKUP" "$PIMCOMP/config.json"' EXIT
cp "$PIMCOMP_CONFIGS/arch-a/latency_config.json" "$PIMCOMP/config.json"
```

The Model Zoo files map exactly to PIMCOMP's bundled model names, so compile
them directly:

```bash
cd "$PIMCOMP/build"

# High-throughput mode; the paper evaluates batches of 128 samples.
./PIMCOMP-NN -m=resnet18 -r=balance -p=batch -o=YES -v=YES -s=YES
./PIMCOMP-NN -m=resnet34 -r=balance -p=batch -o=YES -v=YES -s=YES
./PIMCOMP-NN -m=googlenet -r=balance -p=batch -o=YES -v=YES -s=YES

# Low-latency mode; the paper uses batch size 1.
./PIMCOMP-NN -m=resnet18 -r=balance -p=element -o=YES -v=YES -s=YES
./PIMCOMP-NN -m=resnet34 -r=balance -p=element -o=YES -v=YES -s=YES
./PIMCOMP-NN -m=googlenet -r=balance -p=element -o=YES -v=YES -s=YES
```

VGG-8 first needs PIMCOMP's JSON frontend. Use a temporary ONNX copy because
the released frontend rewrites the input batch dimension in place:

```bash
cd /path/to/Raptor
cp validation/networks/pimcomp_models/vgg8/vgg8-mnist-reconstructed.onnx /tmp/vgg8-pimcomp.onnx
.venv/bin/python third_party/PIMCOMP-NN/frontend/frontend.py \
  --model_path /tmp/vgg8-pimcomp.onnx \
  --save_path third_party/PIMCOMP-NN/models/JSON/vgg8_paper_reconstructed.json

cd third_party/PIMCOMP-NN/build
./PIMCOMP-NN -m=vgg8_paper_reconstructed -r=balance -p=batch -o=YES -v=YES -s=YES
./PIMCOMP-NN -m=vgg8_paper_reconstructed -r=balance -p=element -o=YES -v=YES -s=YES
```

Repeat after selecting `arch-b/latency_config.json` and
`arch-c/latency_config.json`. All four models were
compiled successfully in both modes with all three configs. The released
random placement code occasionally segfaults; an unchanged retry succeeded in
the observed cases.

The paper's optimizer uses a genetic algorithm with population 200 and up to
1000 iterations. The checked-out PIMCOMP submodule already has both paper
settings in `backend/GeneticAlgorithm.h`; select them with `-r=GA`. Fitness
evaluation uses OpenMP and bounded bandwidth timelines. Set `OMP_NUM_THREADS`
to control its parallelism; otherwise OpenMP uses the available CPUs. The GA
uses the fixed seed `1`, so repeated serial and parallel runs are reproducible.

## Compare Raptor and PIMCOMP

The comparison driver uses one random input and one native ONNX-MLIR reference,
compiles both instruction streams, runs both through `pimsim-nn`, runs
functional validation through `pim-simulator`, and writes Markdown and JSON
reports.

To reproduce the complete Arch-A latency experiment, use the model-by-model
runner. It verifies the paper GA settings, builds Raptor and the existing
`third_party/PIMCOMP-NN/build` tree, then runs the `element`/batch-1 comparison
for one model at a time and regenerates `results.csv` from the JSON reports:

```bash
.venv/bin/python validation/tools/pimcomp/run_pimcomp_paper_latency.py
```

Each model directory reuses regular validation's ignored `inputs/`, `outputs/`,
`runner/`, `raptor/`, and `simulation/` paths. PIMCOMP-only artifacts and
`comparison_report.{md,json}` live under `pimcomp/`. The frontend regenerates
one isolated `models/JSON/` graph because PIMCOMP requires that relative
layout; it is removed after a successful backend run and the shared submodule
model directory is never modified. Models requiring BatchNormalization folding
also receive a prepared ONNX file; other models use the original ONNX directly.
PIMCOMP's source tree and build directory remain unchanged at runtime. Use
`--models vgg8` to run one model, `--resume` after an interruption, `--dry-run`
to inspect every command, or `--out-dir PATH` to keep results outside
`validation/`. The runner continues after a failed model so all reports are
produced.

Arch-A low-latency example:

```bash
RAPTOR_ROOT=$PWD

"$RAPTOR_ROOT/.venv/bin/python" "$RAPTOR_ROOT/validation/tools/pimcomp/compare_raptor_pimcomp.py" \
  --model "$RAPTOR_ROOT/validation/networks/pimcomp_models/resnet34/resnet34-v1-7.onnx" \
  --out-dir "$RAPTOR_ROOT/validation/networks/pimcomp_models/resnet34" \
  --pimcomp-config "$RAPTOR_ROOT/validation/pimsim_configs/pimcomp/arch-a/latency_config.json" \
  --core-count 168 \
  --crossbar-count 96 \
  --crossbar-size 128 \
  --mesh-rows 12 \
  --mesh-cols 14 \
  --pimsim-mode latency \
  --pimcomp-pipeline element
```

For Arch-A high throughput, use `--pimsim-mode throughput
--pimcomp-pipeline batch`. For Arch-B, use 138 cores, 128 crossbars, a
`6x23` mesh, and
`validation/pimsim_configs/pimcomp/arch-b/latency_config.json`.

If only semantic and instruction comparison is required, add
`--skip-pimsim-nn`. A VGG-8 run with the same Arch-A LL settings passed both
semantic validations with maximum output differences below `5e-10`.

The comparison runner enables `--fail-on-error`, so a failed compiler,
simulation, or semantic validation makes the command fail while preserving the
generated report.

### Numeric precision and simulator artifacts

The functional and non-functional simulators intentionally consume different
artifacts:

- Raptor and PIMCOMP are validated against the native ONNX-MLIR reference as
  FP32 programs in the Rust simulator. Raptor's emitted program is already
  FP32. The PIMCOMP-to-Rust export expands its element-addressed storage and
  byte-sized transfers to FP32, emits `setbw 32, 32`, and keeps vector
  `imm_len` fields as element counts.
- PIMCOMP's original `SimulationInfo.gz` is copied unchanged for `pimsim-nn`.
  PIMCOMP hardcodes `setbw 8, 8` and one byte per element without performing
  numerical quantization; this artifact is used only for latency estimation.
- Raptor's original FP32 artifact remains unchanged for functional validation.
  A separate `raptor/pimsim_nn/` view uses `setbw 8, 8` and scales its
  byte-addressed storage and transfer sizes from four bytes to one byte per
  element. Vector `imm_len` fields remain element counts. Like PIMCOMP's
  artifact, this view is not numerically valid and is used only for a fair
  non-functional comparison.

The ISA defines vector lengths in elements, while `ld`, `st`, `lldi`, `lmv`,
`send`, `recv`, addresses, and non-vector offsets are byte-based. Do not use
either latency-only artifact for semantic validation.

Current Raptor status:

- VGG-8, ResNet-18, fixed-batch ResNet-34, and GoogLeNet compile on Arch-A.
- Use `googlenet-12-latency.onnx` for the paper-matched latency comparison.
  It removes the two LRN nodes and terminal softmax that PIMCOMP does not
  schedule.
- Raptor currently accepts one square `--crossbar-size`; Arch-C's rectangular
  `512x1024` arrays can therefore be compiled by PIMCOMP but not compared
  exactly with Raptor.

Do not change the hardware profile to bypass either limitation; that would no
longer be a paper-matched comparison.

## Monolith fallback

The local `monolith` SSH alias points to the high-memory host. Copy only this
suite and the comparison driver; `-L` materializes the ResNet-18 symlink because
the canonical `resnetv2/depth_68` file may not exist remotely:

```bash
REMOTE_REPO=/home/gmagnani/Project/Raptor
rsync -azL validation/networks/pimcomp_models/ \
  "monolith:$REMOTE_REPO/validation/networks/pimcomp_models/"
rsync -az validation/pimsim_configs/pimcomp/ \
  "monolith:$REMOTE_REPO/validation/pimsim_configs/pimcomp/"
rsync -az validation/tools/pimcomp/ \
  "monolith:$REMOTE_REPO/validation/tools/pimcomp/"
rsync -az --exclude=.git --exclude=build --exclude=output \
  third_party/PIMCOMP-NN/ \
  "monolith:$REMOTE_REPO/third_party/PIMCOMP-NN/"
```

Then use the same commands over SSH:

```bash
ssh monolith
cd /home/gmagnani/Project/Raptor

# One-time setup if the repository virtual environment is absent.
python3 -m venv .venv
.venv/bin/python -m pip install numpy onnx onnxruntime onnxsim colorama

# Run every latency comparison serially.
.venv/bin/python validation/tools/pimcomp/run_pimcomp_paper_latency.py
```

Copy reports back without transferring large compiler artifacts:

```bash
rsync -az --include='*/' --include='comparison_report.*' --exclude='*' \
  "monolith:$REMOTE_REPO/validation/networks/pimcomp_models/" \
  validation/networks/pimcomp_models/
```