Raptor/validation/networks/pimcomp_models/README.md

PIMCOMP paper models

This directory contains the four networks evaluated in PIMCOMP: An End-to-End DNN Compiler for Processing-In-Memory Accelerators: VGG-8, ResNet-18, ResNet-34, and GoogLeNet.

See the runner-generated 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, with its symbolic batch fixed to 1 as PIMCOMP's frontend does.
googlenet/	GoogLeNet	1x3x224x224	Unmodified ONNX Model Zoo 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:

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/ 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:

.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:

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:

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:

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:

.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:

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:

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:

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:

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