diff --git a/src/kernels/bang/activation.cc b/src/kernels/bang/activation.cc
index 4105b168..7d8f102a 100644
--- a/src/kernels/bang/activation.cc
+++ b/src/kernels/bang/activation.cc
@@ -241,8 +241,50 @@ class HardSigmoidCnnl : public UnaryCnnl {
     float getScale() const override { return 0.5f; }
 };
 
+class LeakyReluCnnl : public BangKernelWithoutConfig {
+    void compute(const Operator &_op,
+                 const RuntimeObj *_context) const override {
+        auto op = as<LeakyReluObj>(_op);
+        auto context = dynamic_cast<const BangRuntimeObj *>(_context);
+
+        void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
+        void *const cData = (op->getOutput()->getRawDataPtr<void *>());
+
+        cnnlTensorDescriptor_t aDesc, cDesc;
+        auto aDim = op->getInputs(0)->getDims();
+        auto cDim = op->getOutput()->getDims();
+        auto coef = op->getAlpha();
+
+        checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
+        checkCnnlError(cnnlSetTensorDescriptor(
+            aDesc, CNNL_LAYOUT_NCHW, cnnlDataTypeConvert(op->getDType()),
+            aDim.size(), aDim.data()));
+        checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
+        checkCnnlError(cnnlSetTensorDescriptor(
+            cDesc, CNNL_LAYOUT_NCHW, cnnlDataTypeConvert(op->getDType()),
+            cDim.size(), cDim.data()));
+        cnnlActivationDescriptor_t opDesc;
+        checkCnnlError(cnnlCreateActivationDescriptor(&opDesc));
+        checkCnnlError(cnnlSetActivationDescriptor_v5(
+            opDesc, CNNL_ACTIVATION_LEAKYRELU, CNNL_ACTIVATION_HIGH_PRECISION,
+            CNNL_NOT_PROPAGATE_NAN, coef, 0.0, 0.0, 0.0, true));
+
+        float alpha = 1.f, beta = 0.f;
+        cnnlStatus_t stat =
+            cnnlActivationForward(context->cnnlHandle(), opDesc, &alpha, aDesc,
+                                  aData, &beta, cDesc, cData);
+        if (stat != CNNL_STATUS_SUCCESS)
+            return;
+        checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
+        checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
+        checkCnnlError(cnnlDestroyActivationDescriptor(opDesc));
+    }
+};
+
 REGISTER_KERNEL(Device::BANG, OpType::Relu, ReluCnnl, "Relu_cnnl_BANG");
 REGISTER_KERNEL(Device::BANG, OpType::PRelu, PReluCnnl, "PRelu_cnnl_BANG");
+REGISTER_KERNEL(Device::BANG, OpType::LeakyRelu, LeakyReluCnnl,
+                "LeakyRelu_cnnl_BANG");
 REGISTER_KERNEL(Device::BANG, OpType::Sigmoid, SigmoidCnnl,
                 "Sigmoid_cnnl_BANG");
 REGISTER_KERNEL(Device::BANG, OpType::Round, RoundCnnl, "Round_cnnl_BANG");
diff --git a/src/kernels/bang/batchnorm.cc b/src/kernels/bang/batchnorm.cc
index 633f0d88..8906fe7a 100644
--- a/src/kernels/bang/batchnorm.cc
+++ b/src/kernels/bang/batchnorm.cc
@@ -16,10 +16,14 @@ class BatchNormCnnl : public BangKernelWithoutConfig {
         void *const bias = (op->getInputs(4)->getRawDataPtr<void *>());
         void *const output = (op->getOutput()->getRawDataPtr<void *>());
 
-        auto dims = op->getInputs(0)->getDims();
-        auto outDims = op->getOutput()->getDims();
-        if (dims.size() != 4)
-            IT_TODO_HALT();
+        auto padDims = [](Shape shape) {
+            for (size_t i = shape.size(); i < 4; ++i) {
+                shape.push_back(1);
+            }
+            return shape;
+        };
+        auto dims = padDims(op->getInputs(0)->getDims());
+        auto outDims = padDims(op->getOutput()->getDims());
 
         int dimsTrans[4] = {dims[0], dims[2], dims[3], dims[1]};
         int dimsOutTrans[4] = {outDims[0], outDims[2], outDims[3], outDims[1]};