Raptor

Raptor is a domain-specific MLIR compiler for neural networks in ONNX format, targeting in-memory computing / processing-in-memory (PIM) architectures. It extends ONNX-MLIR with a PIM accelerator and progressively lowers ONNX-MLIR through custom MLIR dialects to simulator artifacts.

The current target is the PIM simulator stack under backend-simulators/pim. Raptor emits binary per-core .pim instruction files by default, plus memory.bin, config.json, and weight binaries. It can also emit per-core JSON instruction files with --pim-emit-json.

Overview

PIM architectures perform most computation directly in memory. The supported target models a chip with:

• shared host memory,
• multiple PIM cores,
• ReRAM crossbars for vector-matrix / matrix-vector work,
• explicit communication between cores,
• no hardware branch or loop support in emitted simulator code.

Because repeated work such as convolutions is eventually made explicit, emitted instruction counts can grow quickly. Most compiler work therefore focuses on lowering, scheduling, memory layout, and code-generation optimizations.

Targets and simulators

• backend-simulators/pim/pim-simulator is the in-tree Rust functional simulator used by validation. It reads Raptor's pim/ artifact directory and compares simulator output against native ONNX-MLIR execution.
• backend-simulators/pim/pimsim-nn is the non-functional simulator submodule used internally by validation for latency, power, and energy. The helper scripts in pimcomp_utils/ are for comparison with PIMCOMP-NN and contain local paths; treat them as local utilities, not portable workflows.

Compilation pipeline

The PIM sources live under src/PIM and tests under test/PIM. CMake exposes them to ONNX-MLIR through generated shim directories under onnx-mlir/src/Accelerators/PIM and onnx-mlir/test/accelerators/PIM.

High-level lowering flow:

ONNX-MLIR -> Spatial -> Pim (tensor) -> Pim (bufferized) -> PIM artifacts

1. ONNX -> Spatial (src/PIM/Conversion/ONNXToSpatial). Lowers supported ONNX ops into the spat dialect (src/PIM/Dialect/Spatial). Conversion patterns are split by op family under Patterns/{Math,NN,Tensor} and currently cover Conv, Gemm, MatMul, elementwise Add/Mul/Div, ReduceMean, pooling, Relu, Sigmoid, Softmax, Concat, Gather, Reshape, Resize, and Split.

2. Merge compute nodes (src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes). Builds a compute graph, schedules it with the PEFT scheduler, and materializes the merge schedule into Spatial IR. Supporting scheduling code lives under MergeComputeNodes/Scheduling.

3. Spatial -> Pim (src/PIM/Conversion/SpatialToPim). Lowers Spatial operations to the pim dialect (src/PIM/Dialect/Pim), including pim.core, pim.core_batch, communication, tensor packing, global tensor materialization, and return-path normalization.

4. Bufferization (src/PIM/Dialect/Pim/Transforms/Bufferization). Converts tensor-semantics PIM IR into memref-semantics PIM IR using MLIR's bufferization interfaces.

5. PIM local-memory planning (src/PIM/Dialect/Pim/Transforms/LocalMemoryPlanning). Computes whole-core lifetimes, reuses addresses for non-overlapping allocations, and records the explicit plan in PIM IR.

6. PIM verification and code generation (src/PIM/Pass/PimCodegen and src/PIM/Compiler). Verifies the memory plan and other PIM invariants, then emits .pim core files, weights, and memory.bin / config.json without rerunning liveness.

Supporting pieces:

• src/PIM/Common - shared IR, filesystem, diagnostics, reports, and utility helpers.
• src/PIM/Compiler - PIM compiler options, planned-address materialization, binary instruction format, artifact writing, weight emission, and codegen entry points.
• src/PIM/Conversion/SpatialToGraphviz - optional Spatial graphviz conversion pass.
• src/PIM/Pass - pass registration and auxiliary passes.
• src/PIM/PimAccelerator.{cpp,hpp} - ONNX-MLIR accelerator entry point.

PIM compiler options

Pass these to onnx-mlir when compiling for PIM. These are all Raptor/PIM-specific options; onnx-mlir --help lists the inherited ONNX-MLIR options.

• --maccel=PIM - select the PIM accelerator.
• --EmitSpatial, --EmitPim, --EmitPimBufferized, --EmitPimCodegen - stop the PIM pipeline at the requested stage. The PIM default is --EmitPimCodegen.
• --core-count=<N> - required positive core count for PIM compilation.
• --crossbar-size=<N> - crossbar width/height. Default in code is 128.
• --crossbar-count=<N> - crossbars per core. Default in code is 64.
• --pim-memory-report=<summary|none> - emit the concise combined memory report under reports/memory_report.txt, or disable it. Default is summary.
• --pim-only-codegen - assume input is already bufferized PIM IR and only run the codegen tail.
• --pim-emit-json - also emit core_*.json instruction files alongside core_*.pim.
• --pim-export-spatial-dataflow=<none|spatial1|spatial2|spatial3|spatial4|all> - control Spatial dataflow CSV reports for the graph, trivially merged graph, scheduled, and realized snapshots under reports/. Default is none.
• --pim-conv-lowering=<auto|legacy|depthwise|packed-im2col|streamed-patch|streamed-packed|output-channel-tiled|input-k-tiled|tiled-2d> - select the convolution lowering strategy. Default is auto.
• --pim-conv-im2col-max-elements=<N> - maximum globally materialized im2col elements per convolution before streaming. Default is 1048576.
• --pim-conv-stream-chunk-positions=<N> - maximum output positions per streamed convolution chunk. Default is 1024.
• --pim-report-conv-lowering=<true|false> - emit the bounded convolution lowering report. Default is true.
• --use-experimental-conv-impl - use the alternate convolution lowering.
• --pim-detect-communication-deadlock - statically simulate expanded send/receive ordering and reject blocking deadlocks. Default is off.
• --pim-materialize-scalar-fanout-global-order - use the experimental, expensive globally ordered scalar-fanout materializer. Default is off.
• --pim-trace-communication-materialization - emit verbose communication materialization diagnostics and provenance attributes. Default is off.
• --ignore-concat-error - soft-fail a ConcatOp corner case.

Standard PIM hardware profile

Raptor's standard development and YOLO validation profile is:

Parameter	Value
Cores	144
Crossbars per core	64
Crossbar size	128 × 128

Canonical compiler flags:

--crossbar-count=64 --crossbar-size=128 --core-count=144

--core-count remains mandatory and must be passed explicitly to the compiler.

Example:

./build_release/Release/bin/onnx-mlir model.onnx -o /tmp/raptor/model \
  --maccel=PIM --EmitPimCodegen \
  --crossbar-count=64 --crossbar-size=128 --core-count=144

This writes PIM artifacts under /tmp/raptor/pim/.

Validation

Functional validation compiles ONNX models, compares native ONNX-MLIR and PIM simulator outputs, and optionally reports latency and power. See validation/README.md for prerequisites, usage, options, artifacts, and results.

Build

Initialize submodules first:

git submodule update --init --recursive

The project follows ONNX-MLIR's build requirements. The CI workflow documents the currently used versions and setup:

• CMake 4.3.0 in CI,
• LLVM/MLIR checked out under onnx-mlir/llvm-project,
• Protobuf v34.0,
• Rust stable for pim-simulator,
• Python packages numpy, onnx, colorama for validation.

Protobuf

Install Protobuf if your system does not already provide a compatible version:

git clone --depth 1 --branch v34.0 https://github.com/protocolbuffers/protobuf
cmake -S protobuf -B protobuf/build -G Ninja \
  -DCMAKE_BUILD_TYPE=Release \
  -Dprotobuf_BUILD_TESTS=OFF
cmake --build protobuf/build
sudo cmake --install protobuf/build

You can then remove the temporary checkout:

rm -rf protobuf

MLIR

Follow the ONNX-MLIR instructions in onnx-mlir/docs/BuildOnLinuxOSX.md to build LLVM/MLIR. The local Raptor build expects MLIR_DIR to point at the MLIR CMake package, for example:

MLIR_DIR=$(pwd)/onnx-mlir/llvm-project/build_release/lib/cmake/mlir

If your LLVM build directory is named build instead of build_release, adjust the path accordingly.

Raptor

Configure a release build:

MLIR_DIR=$(pwd)/onnx-mlir/llvm-project/build_release/lib/cmake/mlir
cmake -S . -B build_release -G Ninja \
  -DCMAKE_BUILD_TYPE=Release \
  -DONNX_MLIR_ACCELERATORS=PIM \
  -DLLVM_ENABLE_ASSERTIONS=ON \
  -DMLIR_DIR=${MLIR_DIR}

Configure a debug build similarly:

MLIR_DIR=$(pwd)/onnx-mlir/llvm-project/build_debug/lib/cmake/mlir
cmake -S . -B build_debug -G Ninja \
  -DCMAKE_BUILD_TYPE=Debug \
  -DONNX_MLIR_ACCELERATORS=PIM \
  -DLLVM_ENABLE_ASSERTIONS=ON \
  -DMLIR_DIR=${MLIR_DIR}

For debug development, using mold can reduce link time and memory use:

cmake -S . -B build_debug -G Ninja \
  -DCMAKE_BUILD_TYPE=Debug \
  -DONNX_MLIR_ACCELERATORS=PIM \
  -DLLVM_ENABLE_ASSERTIONS=ON \
  -DMLIR_DIR=${MLIR_DIR} \
  -DCMAKE_EXE_LINKER_FLAGS="-fuse-ld=mold" \
  -DCMAKE_SHARED_LINKER_FLAGS="-fuse-ld=mold" \
  -DCMAKE_MODULE_LINKER_FLAGS="-fuse-ld=mold"

Build the compiler with CMake:

cmake --build ./build_release
cmake --build ./build_debug

Do not invoke ninja directly for this project; use cmake --build so CMake's configuration and generated shims stay consistent.

If a build fails because Protobuf headers are missing fixed-width integer definitions, patch the affected Protobuf-generated files by adding #include <cstdint>.

Tests

The Rust simulator has its own tests:

cd backend-simulators/pim/pim-simulator
cargo test

Repository Layout

• src/PIM/ - PIM accelerator implementation.
• test/PIM/ - PIM C++ unit tests.
• validation/ - functional validation scripts, ONNX operation tests, network slices, and pimsim config generation.
• backend-simulators/pim/pim-simulator/ - in-tree Rust functional simulator.
• backend-simulators/pim/pimsim-nn/ - non-functional simulator submodule.
• pimcomp_utils/ - local comparison helpers for PIMCOMP-NN.
• .github/actions/ and .github/workflows/validate_operations.yml - CI setup for MLIR/Protobuf caching, building Raptor, and validation.