diff --git a/python/xmos_ai_tools/xinterpreters/src/dll_interpreter.cc b/python/xmos_ai_tools/xinterpreters/src/dll_interpreter.cc
index 2f76ecea1..9aa571fde 100644
--- a/python/xmos_ai_tools/xinterpreters/src/dll_interpreter.cc
+++ b/python/xmos_ai_tools/xinterpreters/src/dll_interpreter.cc
@@ -90,6 +90,11 @@ DLLEXPORT inference_engine *new_interpreter(size_t max_arena_size, size_t extern
   resolver->AddIf();
   resolver->AddWhile();
   resolver->AddCallOnce();
+  resolver->AddAbs();
+  resolver->AddLog();
+  resolver->AddSqrt();
+  resolver->AddDiv();
+  resolver->AddBatchMatMul();
   tflite_micro::ops::micro::xcore::RegisterXCOps(resolver);
   add_lib_vision_ops(resolver);
 
diff --git a/xformer/IR/XCoreOps.td b/xformer/IR/XCoreOps.td
index d36d60cb2..ba3d60c2d 100644
--- a/xformer/IR/XCoreOps.td
+++ b/xformer/IR/XCoreOps.td
@@ -527,4 +527,17 @@ def XC_Bsign8Op : XC_Op<"bsign_8", [Pure]> {
   let results = (outs TensorOf<[I32]>:$output);
 }
 
+def XC_BatchMatMulOp : XC_Op<"batch_matmul", [Pure]> {
+  let summary = "Batch MatMul op";
+
+  let description = [{Batch MatMul op.}];
+
+  let arguments = (ins TensorOf<[QI8]>:$input1, TensorOf<[QI8]>:$input2,
+      I32ArrayAttr:$compute_shape,
+      F32Attr:$lhs_zero_point, F32Attr:$rhs_zero_point, F32Attr:$out_zero_point,
+      F32Attr:$scale);
+
+  let results = (outs TensorOf<[QI8]>:$output);
+}
+
 #endif // XCORE_DIALECT
diff --git a/xformer/Transforms/Passes.cpp b/xformer/Transforms/Passes.cpp
index d791b4751..4c74b48c7 100644
--- a/xformer/Transforms/Passes.cpp
+++ b/xformer/Transforms/Passes.cpp
@@ -48,6 +48,7 @@ void buildXCoreRemainingPassPipeline(OpPassManager &pm) {
   pm.addPass(createReplaceMaxPoolPass());
   pm.addPass(createReplaceMulPass());
   pm.addPass(createReplaceMeanPass());
+  pm.addPass(createReplaceBatchMatMulPass());
   pm.addPass(createReplaceSumPass());
   pm.addPass(createReplaceTransposeConvPass());
   pm.addPass(createReplaceConv2DPass());
diff --git a/xformer/Transforms/Passes.h b/xformer/Transforms/Passes.h
index 8ed638f50..58815521b 100644
--- a/xformer/Transforms/Passes.h
+++ b/xformer/Transforms/Passes.h
@@ -37,6 +37,7 @@ std::unique_ptr<OperationPass<func::FuncOp>> createRemoveDynamicShapePass();
 std::unique_ptr<OperationPass<func::FuncOp>> createReplaceAddSubPass();
 std::unique_ptr<OperationPass<func::FuncOp>> createReplaceMulPass();
 std::unique_ptr<OperationPass<func::FuncOp>> createReplaceMeanPass();
+std::unique_ptr<OperationPass<func::FuncOp>> createReplaceBatchMatMulPass();
 std::unique_ptr<OperationPass<func::FuncOp>> createReplaceSumPass();
 std::unique_ptr<OperationPass<func::FuncOp>> createReplaceMaxPoolPass();
 std::unique_ptr<OperationPass<func::FuncOp>> createReplaceStridedSlicePass();
diff --git a/xformer/Transforms/ReplaceBatchMatMul.cpp b/xformer/Transforms/ReplaceBatchMatMul.cpp
new file mode 100644
index 000000000..98953ad70
--- /dev/null
+++ b/xformer/Transforms/ReplaceBatchMatMul.cpp
@@ -0,0 +1,172 @@
+#include "IR/XCoreOps.h"
+#include "Utils/Util.h"
+#include "lib_nn/api/AggregateFn.hpp"
+#include "lib_nn/api/MemCpyFn.hpp"
+#include "lib_nn/api/OutputTransformFn.hpp"
+extern "C" {
+#include "lib_nn/api/nn_layers.h"
+}
+#include "mlir/IR/TypeUtilities.h"
+#include "mlir/Pass/Pass.h"
+#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
+#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
+#include "tensorflow/compiler/mlir/lite/utils/validators.h"
+
+namespace mlir::xcore {
+
+namespace {
+// Replace TFL Batch MatMul with Batch MatMul for XCore.
+struct ReplaceBatchMatMul
+    : public PassWrapper<ReplaceBatchMatMul, OperationPass<func::FuncOp>> {
+  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(ReplaceBatchMatMul)
+
+  void getDependentDialects(DialectRegistry &registry) const final {
+    registry.insert<TFL::TensorFlowLiteDialect>();
+  }
+  StringRef getArgument() const final { return "xcore-replace-batch-matmul"; }
+  StringRef getDescription() const final {
+    return "Replace TFL Batch MatMul with Batch MatMul for XCore.";
+  }
+  void runOnOperation() override;
+};
+
+struct ReplaceBatchMatMulPattern : public OpRewritePattern<TFL::BatchMatMulOp> {
+  using OpRewritePattern<TFL::BatchMatMulOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(TFL::BatchMatMulOp batchMatMulOp,
+                                PatternRewriter &rewriter) const override {
+
+    auto X = batchMatMulOp.getX();
+    auto Y = batchMatMulOp.getY();
+    auto output = batchMatMulOp.getOutput();
+    bool memcpyOnX = true;
+    bool reorderOnY = true;
+
+    auto XType = X.getType().cast<ShapedType>();
+    auto YType = Y.getType().cast<ShapedType>();
+    auto outputType = output.getType().cast<ShapedType>();
+    
+    // Check if input and output are int8.
+    bool isInt8 = utils::isNBitSignedQType<8>(XType.getElementType()) &&
+                  utils::isNBitSignedQType<8>(YType.getElementType()) &&
+                  utils::isNBitSignedQType<8>(outputType.getElementType());
+
+    if (!isInt8) {
+      return failure();
+    }
+
+    auto XShape = XType.getShape();
+    if (XShape.size() != 3) {
+      return mlir::failure(); // We don't support batch matmul with rank != 3
+    }
+
+    auto YShape = YType.getShape();
+    if (YShape.size() != 3) {
+      return mlir::failure(); // We don't support batch matmul with rank != 3
+    }
+
+    if (XShape[0] != YShape[0] || XShape[2] != YShape[1]) {
+      return mlir::failure(); // X Y shape not match
+    }
+
+    int64_t B = XShape[0];
+    int64_t N = XShape[1];
+    int64_t M = YShape[1];
+    int64_t K = YShape[2];
+
+    auto XConstOp = dyn_cast_or_null<TFL::QConstOp>(X.getDefiningOp());
+    auto YConstOp = dyn_cast_or_null<TFL::QConstOp>(Y.getDefiningOp());
+    
+    if (XConstOp) {
+      llvm::outs() << "const op on x\n";
+    }
+
+    if (YConstOp) {
+      llvm::outs() << "const op on y\n";
+    }
+
+    if (XConstOp && YConstOp) {
+      // Shouldn't be both const
+      return failure();
+    }
+
+    bool adjX = batchMatMulOp.getAdjX();
+    bool adjY = batchMatMulOp.getAdjY();
+
+    // Here we take the chance to reorder X or Y if they are const
+    if (YConstOp) {
+      auto valueAttr = YConstOp.getValue().cast<DenseElementsAttr>();
+      std::vector<int8_t> dataVec = std::vector<int8_t>(
+        valueAttr.getValues<int8_t>().begin(), valueAttr.getValues<int8_t>().end());
+      llvm::outs() << "shape " << B << " " << M << " " << K << " total: " << dataVec.size() << "\n";
+      llvm::outs() << "new shape " << B << " " << K << " " << M << "\n";
+
+      if (adjY == false) {
+        // Reorder to column-major (B, K, M), adjY == true means it's already column-major
+        std::vector<int8_t> oldVec = dataVec;
+        dataVec = std::vector<int8_t>(B*M*K);
+        for (int64_t b = 0; b < B; ++b) {
+          for (int64_t m = 0; m < M; ++m) {
+            for (int64_t k = 0; k < K; ++k) {
+              dataVec[b * (K * M) + k * M + m] = oldVec[b * (K * M) + m * K + k];
+            }
+          }
+        }
+      }
+
+      // Create new constant attribute with shape [B, K, M]
+      TypeAttr newTypeAttr = TypeAttr::get(
+          RankedTensorType::get(
+            {B, K, M}, YConstOp.getQtype().cast<RankedTensorType>().getElementType()));
+      DenseElementsAttr newValueAttr = DenseElementsAttr::get<int8_t>(
+          RankedTensorType::get(
+            {B, K, M}, rewriter.getIntegerType(8)), dataVec);
+      Y = rewriter.create<TFL::QConstOp>(
+          batchMatMulOp.getLoc(), newTypeAttr, newValueAttr);
+      reorderOnY = false;
+    }
+
+    auto xQType = utils::getQType(X);
+    auto yQType = utils::getQType(Y);
+    auto outputQType = utils::getQType(output);
+
+    float xZeroPoint = static_cast<float>(xQType.getZeroPoint());
+    float yZeroPoint = static_cast<float>(yQType.getZeroPoint());
+    float outZeroPoint = static_cast<float>(outputQType.getZeroPoint());
+    float xScale = static_cast<float>(xQType.getScale());
+    float yScale = static_cast<float>(yQType.getScale());
+    float outScale = static_cast<float>(outputQType.getScale());
+
+    int32_t computeShape[4] = {B, N, M, K};  // batch, lhs row size, channel_size, rhs col size
+
+    auto xcBatchMatMulOp = rewriter.create<BatchMatMulOp>(
+        batchMatMulOp.getLoc(), batchMatMulOp.getType(), X, Y,
+        rewriter.getI32ArrayAttr(computeShape),
+        rewriter.getF32FloatAttr(xZeroPoint),
+        rewriter.getF32FloatAttr(yZeroPoint),
+        rewriter.getF32FloatAttr(outZeroPoint),
+        rewriter.getF32FloatAttr(xScale*yScale/outScale)
+      );
+    rewriter.replaceOp(batchMatMulOp, xcBatchMatMulOp.getOutput());
+    
+    return success();
+  }
+};
+
+void ReplaceBatchMatMul::runOnOperation() {
+  auto *ctx = &getContext();
+  func::FuncOp func = getOperation();
+  RewritePatternSet patterns(ctx);
+  patterns.insert<ReplaceBatchMatMulPattern>(ctx);
+  (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
+}
+} // namespace
+
+// Creates an instance of the ReplaceBatchMatMul pass.
+std::unique_ptr<OperationPass<func::FuncOp>> createReplaceBatchMatMulPass() {
+  return std::make_unique<ReplaceBatchMatMul>();
+}
+
+static PassRegistration<ReplaceBatchMatMul> pass;
+
+} // namespace mlir::xcore
diff --git a/xformer/Transforms/TranslateToCustomOp.cpp b/xformer/Transforms/TranslateToCustomOp.cpp
index 23756e020..caa227578 100644
--- a/xformer/Transforms/TranslateToCustomOp.cpp
+++ b/xformer/Transforms/TranslateToCustomOp.cpp
@@ -281,6 +281,24 @@ std::vector<uint8_t> MaxPool2DOp::buildCustomOptions() {
   return fbb.GetBuffer();
 }
 
+std::vector<uint8_t> BatchMatMulOp::buildCustomOptions() {
+  flexbuffers::Builder fbb;
+  auto rootMap = fbb.StartMap();
+  auto computeShapeVec = fbb.StartVector("compute_shape");
+  auto computeShape = getComputeShape().cast<ArrayAttr>();
+  for (int i = 0; i < 4; ++i) {
+    fbb.Int(computeShape[i].cast<IntegerAttr>().getInt());
+  }
+  fbb.EndVector(computeShapeVec, false, false);
+  fbb.IndirectFloat("lhs_zp", getLhsZeroPoint().convertToFloat());
+  fbb.IndirectFloat("rhs_zp", getRhsZeroPoint().convertToFloat());
+  fbb.IndirectFloat("out_zp", getOutZeroPoint().convertToFloat());
+  fbb.IndirectFloat("scale", getScale().convertToFloat());
+  fbb.EndMap(rootMap);
+  fbb.Finish();
+  return fbb.GetBuffer();
+}
+
 namespace {
 /// This pass translates XCore ops to TFLite custom ops.
 struct TranslateToCustomOp
@@ -330,6 +348,7 @@ void TranslateToCustomOp::runOnOperation() {
   patterns.insert<RewriteToCustomOp<MulOp>>(ctx);
   patterns.insert<RewriteToCustomOp<MeanOp>>(ctx);
   patterns.insert<RewriteToCustomOp<MeanI16Op>>(ctx);
+  patterns.insert<RewriteToCustomOp<BatchMatMulOp>>(ctx);
   patterns.insert<RewriteToCustomOp<Pad3To4Op>>(ctx);
   patterns.insert<RewriteToCustomOp<Pad1To4Op>>(ctx);
   patterns.insert<RewriteToCustomOp<SliceOp>>(ctx);
diff --git a/xformer/lib_tflite_micro.BUILD b/xformer/lib_tflite_micro.BUILD
index 1c327eb7f..2e9fde6a1 100644
--- a/xformer/lib_tflite_micro.BUILD
+++ b/xformer/lib_tflite_micro.BUILD
@@ -55,5 +55,6 @@ filegroup(
         "lib_tflite_micro/src/tflite-xcore-kernels/xcore_beta_transposeconvf32.cc",
         "lib_tflite_micro/src/tflite-xcore-kernels/xcore_beta_fcf32.cc",
         "lib_tflite_micro/src/tflite-xcore-kernels/conv2d_float.c",
+        "lib_tflite_micro/src/tflite-xcore-kernels/xcore_batch_matmul.cc",
     ],
 )
diff --git a/xformer/lib_tflmc.BUILD b/xformer/lib_tflmc.BUILD
index 3a6f90370..e55d77493 100644
--- a/xformer/lib_tflmc.BUILD
+++ b/xformer/lib_tflmc.BUILD
@@ -67,6 +67,7 @@ filegroup(
         "@tflite_micro//tensorflow/lite/micro/kernels:slice.cc",
         "@tflite_micro//tensorflow/lite/micro/kernels:arg_min_max.cc",
         "@tflite_micro//tensorflow/lite/micro/kernels:batch_to_space_nd.cc",
+        "@tflite_micro//tensorflow/lite/micro/kernels:batch_matmul.cc",
         "@tflite_micro//tensorflow/lite/micro/kernels:broadcast_args.cc",
         "@tflite_micro//tensorflow/lite/micro/kernels:broadcast_to.cc",
         "@tflite_micro//tensorflow/lite/micro/kernels:cast.cc",