diff --git a/dnn/src/arm_common/conv_bias/opr_impl.cpp b/dnn/src/arm_common/conv_bias/opr_impl.cpp
index e7738120c6b01f26f074fefc80aa8ac8671321f4..f7edf5956b620176effeddf0d7ee3d316ae51775 100644
--- a/dnn/src/arm_common/conv_bias/opr_impl.cpp
+++ b/dnn/src/arm_common/conv_bias/opr_impl.cpp
@@ -154,7 +154,7 @@ public:
         for (auto&& algo : matmul_algos) {
             if (algo->type() == nullptr)
                 continue;
-            for (uint32_t tile_size : {8, 16, 24, 32, 40, 48, 64, 80}) {
+            for (uint32_t tile_size : {16, 8, 24, 32}) {
                 refhold.emplace_back(new AlgoFP32WinogradF23_4x4(
                         static_cast<fallback::MatrixMulImpl::AlgoBase*>(algo),
                         tile_size));
diff --git a/src/gopt/impl/framework.cpp b/src/gopt/impl/framework.cpp
index 6f5e9e81bce3c0feb8210f11774a48d1d422a7df..a49f824737d919443c6404cfce9e6d05798302da 100644
--- a/src/gopt/impl/framework.cpp
+++ b/src/gopt/impl/framework.cpp
@@ -725,6 +725,7 @@ const GraphOptimizer& GraphOptimizer::add_passes_for_optimize_options(
     cb(f16_io_comp, { add_pass(ConvertF32ToF16Pass::make(false)); });
     cb(f16_io_f32_comp, { add_pass(ConvertF32ToF16Pass::make(true)); });
 
+
     cb(nchw4, {
         add_pass<FuseConvBiasNonlinPass>();
         add_pass<FuseConvBiasZPass>();
@@ -736,10 +737,21 @@ const GraphOptimizer& GraphOptimizer::add_passes_for_optimize_options(
         add_pass<FuseConvBiasNonlinPass>();
         add_pass(ConvertFormatPass::make_nhwcd4_converter());
     });
-    cb(nchw88, { add_pass(EnableNchwxxPass::make_nchwxx_converter(8)); });
-    cb(nchw44, { add_pass(EnableNchwxxPass::make_nchwxx_converter(4)); });
-    cb(nchw44_dot,
-       { add_pass(EnableNchw44DotPass::make_nchw44_dot_converter()); });
+    cb(nchw88, {
+        add_pass<FuseConvBiasNonlinPass>();
+        add_pass(EnableNchwxxPass::make_nchwxx_converter(8));
+        add_pass<ShuffleShuffleRemovePass>();
+    });
+    cb(nchw44, {
+        add_pass<FuseConvBiasNonlinPass>();
+        add_pass(EnableNchwxxPass::make_nchwxx_converter(4));
+        add_pass<ShuffleShuffleRemovePass>();
+    });
+    cb(nchw44_dot, {
+        add_pass<FuseConvBiasNonlinPass>();
+        add_pass(EnableNchw44DotPass::make_nchw44_dot_converter());
+        add_pass<ShuffleShuffleRemovePass>();
+    });
     cb(nchw32, {
         add_pass<FuseConvBiasNonlinPass>();
         add_pass<FuseConvBiasZPass>();
diff --git a/src/opr/impl/dnn/convolution.cpp b/src/opr/impl/dnn/convolution.cpp
index 9f72c3f71fbd3cc29abe1dc2064b6926bd270301..6db500041653d34ae69dde862a6d57c7669017fa 100644
--- a/src/opr/impl/dnn/convolution.cpp
+++ b/src/opr/impl/dnn/convolution.cpp
@@ -707,7 +707,9 @@ template <>
 void AlgoChooser<megdnn::ConvBias>::ExeContext::
         modify_param_with_weights_preprocessed(
                 typename TimedProfiler<megdnn::ConvBias>::Param& param) const {
-    if (param.opr_param.format == megdnn::ConvBias::Param::Format::NCHW) {
+    if (param.opr_param.format == megdnn::ConvBias::Param::Format::NCHW ||
+        param.opr_param.format == megdnn::ConvBias::Param::Format::NCHW44 ||
+        param.opr_param.format == megdnn::ConvBias::Param::Format::NCHW88) {
         auto winograd_param =
                 megdnn::ConvBias::parse_winograd_name(param.algo_name);
         if (winograd_param == megdnn::ConvBias::INVALID_WINOGRAD_PARAM) {
@@ -727,8 +729,18 @@ void AlgoChooser<megdnn::ConvBias>::ExeContext::
                                                filter_transform_layout);
         param.shapes[1] = filter_transform_layout;
         param.dtypes[1] = filter_transform_layout.dtype.enumv();
-
-        param.opr_param.format = megdnn::ConvBias::Param::Format::NCHW_WINOGRAD;
+        if (param.opr_param.format == megdnn::ConvBias::Param::Format::NCHW) {
+            param.opr_param.format =
+                    megdnn::ConvBias::Param::Format::NCHW_WINOGRAD;
+        } else if (param.opr_param.format ==
+                   megdnn::ConvBias::Param::Format::NCHW44) {
+            param.opr_param.format =
+                    megdnn::ConvBias::Param::Format::NCHW44_WINOGRAD;
+        } else if (param.opr_param.format ==
+                   megdnn::ConvBias::Param::Format::NCHW) {
+            param.opr_param.format =
+                    megdnn::ConvBias::Param::Format::NCHW88_WINOGRAD;
+        }
         param.opr_param.output_block_size = winograd_param.output_block_size;
     }
 }
diff --git a/src/plugin/impl/opr_footprint.cpp b/src/plugin/impl/opr_footprint.cpp
index eaabb8cf83da38cc1de43c1f7fabf55419d278b6..22111a7dbf7d07f181c770d1fab8d3ffbd8ef431 100644
--- a/src/plugin/impl/opr_footprint.cpp
+++ b/src/plugin/impl/opr_footprint.cpp
@@ -160,6 +160,7 @@ uint64_t eval_conv_computation(const TensorShape& src_shape,
             spatial_start = 2;
             break;
         case Param::Format::NCHW_WINOGRAD:
+        case Param::Format::NCHW44_WINOGRAD:
         case Param::Format::NCHW88_WINOGRAD:
             cpos = 1;
             spatial_start = 0;
@@ -191,9 +192,10 @@ uint64_t eval_conv_computation(const TensorShape& src_shape,
     uint64_t fh = static_cast<uint64_t>(filter_shape[spatial_start]);
     uint64_t fw = static_cast<uint64_t>(filter_shape[spatial_start + 1]);
     if (param.format == Param::Format::NCHW_WINOGRAD ||
+        param.format == Param::Format::NCHW44_WINOGRAD ||
         param.format == Param::Format::NCHW88_WINOGRAD) {
         mgb_assert(opr->same_type<opr::ConvBias>(),
-                   "Only conv bias support NCHW_WINOGRAD");
+                   "Only conv bias support WINOGRAD");
         auto&& conv_bias_opr = opr->cast_final_safe<opr::ConvBias>();
         uint32_t output_block_size = conv_bias_opr.param().output_block_size;
         mgb_assert(fh == fw,
@@ -208,6 +210,10 @@ uint64_t eval_conv_computation(const TensorShape& src_shape,
         return dst_shape.total_nr_elems() * fh * fw *
                static_cast<uint64_t>(src_shape[cpos] * 8) / group * 2;
     }
+    if (param.format == Param::Format::NCHW44_WINOGRAD) {
+        return dst_shape.total_nr_elems() * fh * fw *
+               static_cast<uint64_t>(src_shape[cpos] * 4) / group * 2;
+    }
     return dst_shape.total_nr_elems() * fh * fw *
            static_cast<uint64_t>(src_shape[cpos]) / group * 2;
 }