diff --git a/src/PIM/Conversion/ONNXToSpatial/Common.hpp b/src/PIM/Conversion/ONNXToSpatial/Common.hpp
index 351a54b..da5626e 100644
--- a/src/PIM/Conversion/ONNXToSpatial/Common.hpp
+++ b/src/PIM/Conversion/ONNXToSpatial/Common.hpp
@@ -1,16 +1,19 @@
 #pragma once
 
+#include "mlir/Dialect/Arith/IR/Arith.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/IR/Block.h"
 #include "mlir/IR/BuiltinTypes.h"
 #include "mlir/IR/ValueRange.h"
-#include "mlir/Support/LLVM.h"
 #include "mlir/Transforms/DialectConversion.h"
 
 #include <cassert>
 #include <type_traits>
 #include <utility>
 
+#include "llvm/ADT/SmallPtrSet.h"
+
+#include "src/Accelerators/PIM/Common/PimCommon.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
 
@@ -102,6 +105,42 @@ inline auto getTensorShape(mlir::Value tensor) {
   return mlir::cast<mlir::RankedTensorType>(tensor.getType()).getShape();
 }
 
+inline bool isWeightLikeComputeOperand(mlir::Value value) {
+  auto rankedType = mlir::dyn_cast<mlir::RankedTensorType>(value.getType());
+  if (!rankedType || !isMatrixShape(rankedType.getShape()))
+    return false;
+
+  llvm::SmallPtrSet<mlir::Operation*, 8> visited;
+
+  while (auto* definingOp = value.getDefiningOp()) {
+    if (!visited.insert(definingOp).second)
+      return false;
+    if (hasWeightAlways(definingOp))
+      return true;
+
+    if (auto extractSliceOp = mlir::dyn_cast<mlir::tensor::ExtractSliceOp>(definingOp)) {
+      value = extractSliceOp.getSource();
+      continue;
+    }
+    if (auto expandShapeOp = mlir::dyn_cast<mlir::tensor::ExpandShapeOp>(definingOp)) {
+      value = expandShapeOp.getSrc();
+      continue;
+    }
+    if (auto collapseShapeOp = mlir::dyn_cast<mlir::tensor::CollapseShapeOp>(definingOp)) {
+      value = collapseShapeOp.getSrc();
+      continue;
+    }
+    if (auto transposeOp = mlir::dyn_cast<mlir::ONNXTransposeOp>(definingOp)) {
+      value = transposeOp.getData();
+      continue;
+    }
+
+    return false;
+  }
+
+  return false;
+}
+
 namespace detail {
 
 inline mlir::ValueRange getBlockArgs(mlir::Block* block) { return mlir::ValueRange(block->getArguments()); }
@@ -111,6 +150,11 @@ decltype(auto) invokeWithBlockArgs(Fn&& fn, mlir::Block* block, std::index_seque
   return std::forward<Fn>(fn)(block->getArgument(Is)...);
 }
 
+template <typename Fn, size_t... Is>
+decltype(auto) invokeWithValues(Fn&& fn, mlir::ArrayRef<mlir::Value> values, std::index_sequence<Is...>) {
+  return std::forward<Fn>(fn)(values[Is]...);
+}
+
 template <size_t>
 using ValueArg = mlir::Value;
 
diff --git a/src/PIM/Conversion/ONNXToSpatial/ONNXToSpatialPass.cpp b/src/PIM/Conversion/ONNXToSpatial/ONNXToSpatialPass.cpp
index f6e56c6..908cae6 100644
--- a/src/PIM/Conversion/ONNXToSpatial/ONNXToSpatialPass.cpp
+++ b/src/PIM/Conversion/ONNXToSpatial/ONNXToSpatialPass.cpp
@@ -53,6 +53,7 @@ private:
   void annotateWeightsConstants(func::FuncOp funcOp) const;
   void encapsulateGlobalInstruction(func::FuncOp funcOp);
   void mergeTriviallyConnectedComputes(func::FuncOp funcOp);
+  LogicalResult promoteConstantInputsToWeights(func::FuncOp funcOp);
 };
 
 } // namespace
@@ -149,6 +150,12 @@ void ONNXToSpatialPass::runOnOperation() {
 
   annotateWeightsConstants(*entryFunc);
   encapsulateGlobalInstruction(*entryFunc);
+
+  if (failed(promoteConstantInputsToWeights(*entryFunc))) {
+    signalPassFailure();
+    return;
+  }
+
   mergeTriviallyConnectedComputes(*entryFunc);
 
   // Dump to file for debug
@@ -184,8 +191,8 @@ bool encapsulateConcat(IRRewriter& rewriter, Location loc, Operation* inst) {
     if (llvm::any_of(
           sources, [](auto source) { return isa_and_present<spatial::SpatWeightedCompute>(source.getDefiningOp()); })) {
       auto newCompute = spatial::SpatWeightedCompute::create(rewriter, loc, inst->getResultTypes().front(), sources);
-      llvm::SmallVector<Type> sourceTypes;
-      llvm::SmallVector<Location> sourceLoc;
+      SmallVector<Type> sourceTypes;
+      SmallVector<Location> sourceLoc;
       for (auto source : sources) {
         sourceTypes.push_back(source.getType());
         sourceLoc.push_back(loc);
@@ -206,6 +213,63 @@ bool encapsulateConcat(IRRewriter& rewriter, Location loc, Operation* inst) {
   return false;
 }
 
+static FailureOr<Value> materializeWeightLikeValueInBlock(Value value, IRRewriter& rewriter, IRMapping& mapper) {
+  if (auto mapped = mapper.lookupOrNull(value))
+    return cast<Value>(mapped);
+
+  Operation* definingOp = value.getDefiningOp();
+  if (!definingOp)
+    return failure();
+
+  if (isa<arith::ConstantOp, ONNXConstantOp>(definingOp)) {
+    auto tensorType = dyn_cast<RankedTensorType>(value.getType());
+    if (!tensorType || !tensorType.hasStaticShape())
+      return failure();
+
+    SmallVector<OpFoldResult> offsets(tensorType.getRank(), rewriter.getIndexAttr(0));
+    SmallVector<OpFoldResult> sizes;
+    SmallVector<OpFoldResult> strides(tensorType.getRank(), rewriter.getIndexAttr(1));
+    sizes.reserve(tensorType.getRank());
+    for (int64_t dim : tensorType.getShape())
+      sizes.push_back(rewriter.getIndexAttr(dim));
+
+    auto referencedValue =
+      tensor::ExtractSliceOp::create(rewriter, value.getLoc(), tensorType, value, offsets, sizes, strides);
+    mapper.map(value, referencedValue.getResult());
+    return referencedValue.getResult();
+  }
+
+  if (!isa<tensor::ExtractSliceOp, tensor::ExpandShapeOp, tensor::CollapseShapeOp, ONNXTransposeOp>(definingOp))
+    return failure();
+
+  IRMapping localMapper;
+  for (Value operand : definingOp->getOperands()) {
+    if (auto mapped = mapper.lookupOrNull(operand)) {
+      localMapper.map(operand, cast<Value>(mapped));
+      continue;
+    }
+
+    if (isWeightLikeComputeOperand(operand)) {
+      auto clonedOperand = materializeWeightLikeValueInBlock(operand, rewriter, mapper);
+      if (failed(clonedOperand))
+        return failure();
+      localMapper.map(operand, *clonedOperand);
+      continue;
+    }
+
+    localMapper.map(operand, operand);
+  }
+
+  Operation* clonedOp = rewriter.clone(*definingOp, localMapper);
+  for (auto [oldResult, newResult] : llvm::zip(definingOp->getResults(), clonedOp->getResults()))
+    mapper.map(oldResult, newResult);
+
+  auto mapped = mapper.lookupOrNull(value);
+  if (!mapped)
+    return failure();
+  return cast<Value>(mapped);
+}
+
 // TODO what we want to keep in global?
 void ONNXToSpatialPass::encapsulateGlobalInstruction(func::FuncOp funcOp) {
   Location loc = funcOp.getLoc();
@@ -328,6 +392,85 @@ void ONNXToSpatialPass::annotateWeightsConstants(func::FuncOp funcOp) const {
   });
 }
 
+LogicalResult ONNXToSpatialPass::promoteConstantInputsToWeights(func::FuncOp funcOp) {
+  IRRewriter rewriter(&getContext());
+  SmallVector<spatial::SpatWeightedCompute> computes(funcOp.getOps<spatial::SpatWeightedCompute>());
+
+  for (auto compute : computes) {
+    SmallVector<bool> promoteInput(compute.getInputs().size(), false);
+    bool needsRewrite = false;
+    for (auto [inputIdx, input] : llvm::enumerate(compute.getInputs())) {
+      if (!isWeightLikeComputeOperand(input))
+        continue;
+      promoteInput[inputIdx] = true;
+      needsRewrite = true;
+    }
+    if (!needsRewrite)
+      continue;
+
+    rewriter.setInsertionPointAfter(compute);
+
+    SmallVector<Value> newWeights(compute.getWeights().begin(), compute.getWeights().end());
+    SmallVector<Value> newInputs;
+    SmallVector<Type> newInputTypes;
+    SmallVector<Location> newInputLocs;
+    newWeights.reserve(compute.getWeights().size() + compute.getInputs().size());
+    newInputs.reserve(compute.getInputs().size());
+    newInputTypes.reserve(compute.getInputs().size());
+    newInputLocs.reserve(compute.getInputs().size());
+
+    for (auto [inputIdx, input] : llvm::enumerate(compute.getInputs())) {
+      if (promoteInput[inputIdx]) {
+        newWeights.push_back(input);
+        continue;
+      }
+      newInputs.push_back(input);
+      newInputTypes.push_back(input.getType());
+      newInputLocs.push_back(input.getLoc());
+    }
+
+    auto newCompute =
+      spatial::SpatWeightedCompute::create(rewriter, compute.getLoc(), compute.getResultTypes(), newWeights, newInputs);
+    auto* newBlock =
+      rewriter.createBlock(&newCompute.getBody(), newCompute.getBody().end(), newInputTypes, newInputLocs);
+    newCompute.getProperties().setOperandSegmentSizes(
+      {static_cast<int>(newWeights.size()), static_cast<int>(newInputs.size())});
+    rewriter.setInsertionPointToStart(newBlock);
+
+    IRMapping mapper;
+    auto& oldBlock = compute.getBody().front();
+    size_t newInputIdx = 0;
+    for (auto [oldInputIdx, oldArg] : llvm::enumerate(oldBlock.getArguments())) {
+      if (!promoteInput[oldInputIdx]) {
+        mapper.map(oldArg, newBlock->getArgument(newInputIdx++));
+        continue;
+      }
+
+      auto clonedValue = materializeWeightLikeValueInBlock(compute.getInputs()[oldInputIdx], rewriter, mapper);
+      if (failed(clonedValue))
+        return compute.emitError("failed to materialize promoted weight-like operand inside compute body");
+      mapper.map(oldArg, *clonedValue);
+    }
+
+    for (auto& op : oldBlock.without_terminator())
+      rewriter.clone(op, mapper);
+
+    auto oldYield = cast<spatial::SpatYieldOp>(oldBlock.getTerminator());
+    SmallVector<Value> newYieldOperands;
+    newYieldOperands.reserve(oldYield.getOutputs().size());
+    for (Value operand : oldYield.getOutputs()) {
+      auto mapped = mapper.lookupOrNull(operand);
+      newYieldOperands.push_back(mapped ? cast<Value>(mapped) : operand);
+    }
+    spatial::SpatYieldOp::create(rewriter, oldYield.getLoc(), newYieldOperands);
+
+    compute.replaceAllUsesWith(newCompute);
+    compute.erase();
+  }
+
+  return success();
+}
+
 std::unique_ptr<Pass> createONNXToSpatialPass() { return std::make_unique<ONNXToSpatialPass>(); }
 
 } // namespace onnx_mlir