diff --git a/current.txt b/current.txt
index 339df2f..2e3f198 100644
--- a/current.txt
+++ b/current.txt
@@ -594,11 +594,11 @@
 2ce820dc4f3c6d85721b65150ed2157c6e2e2055f866fb6c6ba4790f14408d66 android.hardware.camera.provider@2.4::ICameraProviderCallback
 b69a7615c508acf5c5201efd1bfa3262167874fc3594e2db5a3ff93addd8ac75 android.hardware.keymaster@4.0::IKeymasterDevice
 eb2fa0c883c2185d514be0b84c179b283753ef0c1b77b45b4f359bd23bba8b75 android.hardware.neuralnetworks@1.0::IPreparedModel
-8eac60e1f724d141c71c69f06d4544acb720a55dfbbcd97fa01bb3d25ee4e2f5 android.hardware.neuralnetworks@1.0::types
+92e101b30e47bdf526a01c52cecfbe730def5997b8260ab497eb949eb2a6dcdf android.hardware.neuralnetworks@1.0::types
 5f6d3097ba84cb63c430787123f4de1b31c11f90b531b98eae9a8623a5ae962a android.hardware.neuralnetworks@1.1::types
 fb382e986c10b8fbb797a8546e8f9ea6d1107bfe6f3fb7e57f6bbbf1f807a906 android.hardware.neuralnetworks@1.2::IDevice
 40e71cd693de5b832325c5d8f081f2ff20a7ba2b89d401cee5b4b3eb0e241681 android.hardware.neuralnetworks@1.2::IPreparedModel
-00649d29680f2c47edf60000c3ae7ae906ba638f0616947147e3676a83cf36fa android.hardware.neuralnetworks@1.2::types
+ee1a0dee5be00a6fe2d4d3270068c78016dcb194d768fe07ed894ea20904037f android.hardware.neuralnetworks@1.2::types
 a785a57447a81e9c130eef6904c3a5c256076c6a04588c40620ebd6fa2660d77 android.hardware.radio@1.2::types
 1a6e2bd289f22931c526b21916910f1d4c436b7acb9556e4243de4ce8e6cc2e4 android.hardware.soundtrigger@2.0::ISoundTriggerHwCallback
 fd65298e1e09e0e3c781ab18305920d757dbe55a3b459ce17814ec5cf6dfee99 android.hardware.wifi@1.0::IWifiP2pIface
@@ -715,7 +715,7 @@
 6e904be0ddca5ae1de8eba020e6c38ed935ea7d80cd08f47787f137a0ca58555 android.hardware.neuralnetworks@1.3::IFencedExecutionCallback
 2b0b10d2ea7a18a4048cd0eb83d35c19a817aeee95f65807fc31f4ef21381397 android.hardware.neuralnetworks@1.3::IPreparedModel
 eee3430cc86c97c7b407495863d8fb61da6f1a64b7721e77b9b4909b11b174e9 android.hardware.neuralnetworks@1.3::IPreparedModelCallback
-e442ab1b440327fe4e8a3b0b8ac6874e9bc6342e91fe976eb9fea77c63961ec8 android.hardware.neuralnetworks@1.3::types
+acf84925f8ee0a651f2ec547ac334034de266479b93af5434f6c1f25e66aba96 android.hardware.neuralnetworks@1.3::types
 b454df853441c12f6e425e8a60dd29fda20f5e6e39b93d1103e4b37495db38aa android.hardware.radio@1.5::IRadio
 fcbb0742a88215ee7a6d7ce0825d253eb2b50391fc6c8c48667f9fd7f6d4549e android.hardware.radio@1.5::IRadioIndication
 b809193970a91ca637a4b0184767315601d32e3ef3d5992ffbc7a8d14a14f015 android.hardware.radio@1.5::IRadioResponse
diff --git a/neuralnetworks/1.0/types.hal b/neuralnetworks/1.0/types.hal
index 1175a30..620eefb 100644
--- a/neuralnetworks/1.0/types.hal
+++ b/neuralnetworks/1.0/types.hal
@@ -261,7 +261,7 @@
      *      filter.
      * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
      *      tensor of type {@link OperandType::TENSOR_FLOAT32}
-     *      the bias must be of the same type.
+     *       the bias must be of the same type.
      *      For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
      *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
      *      of 0 and bias_scale == input_scale * filter_scale.
@@ -289,7 +289,7 @@
      *      filter.
      * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
      *      tensor of type {@link OperandType::TENSOR_FLOAT32}
-     *      the bias must be of the same
+     *       the bias must be of the same
      *      type.
      *      For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
      *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
@@ -356,7 +356,7 @@
      *      specifying the filter.
      * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
      *      tensor of type {@link OperandType::TENSOR_FLOAT32}
-     *      the bias must be of the same type.
+     *       the bias must be of the same type.
      *      For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
      *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
      *      of 0 and bias_scale == input_scale * filter_scale.
@@ -385,7 +385,7 @@
      *      specifying the filter.
      * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
      *      tensor of type {@link OperandType::TENSOR_FLOAT32}
-     *      the bias must be of the same type.
+     *       the bias must be of the same type.
      *      For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
      *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
      *      of 0 and bias_scale == input_scale * filter_scale.
@@ -628,7 +628,7 @@
     HASHTABLE_LOOKUP = 10,
 
     /**
-     * Applies L2 normalization along the depth dimension.
+     * Applies L2 normalization along the axis dimension.
      *
      * The values in the output tensor are computed as:
      *
diff --git a/neuralnetworks/1.2/types.hal b/neuralnetworks/1.2/types.hal
index f0fd769..2c3c599 100644
--- a/neuralnetworks/1.2/types.hal
+++ b/neuralnetworks/1.2/types.hal
@@ -846,7 +846,7 @@
     HASHTABLE_LOOKUP = @1.1::OperationType:HASHTABLE_LOOKUP,
 
     /**
-     * Applies L2 normalization along the depth dimension.
+     * Applies L2 normalization along the axis dimension.
      *
      * The values in the output tensor are computed as:
      *
@@ -854,8 +854,7 @@
      *         input[batch, row, col, channel] /
      *         sqrt(sum_{c} pow(input[batch, row, col, c], 2))
      *
-     * For input tensor with rank less than 4, independently normalizes each
-     * 1-D slice along dimension dim.
+     * By default the axis dimension is the last dimension of the input tensor.
      *
      * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
@@ -3843,7 +3842,8 @@
      * * 1: A scalar {@link OperandType::INT32}, specifying the number of
      *      independent samples to draw for each row slice.
      * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [2],
-     *      specifying seeds used to initialize the random distribution.
+     *      specifying seeds used to initialize the random distribution. If both
+     *      provided seeds are 0, both will be randomly generated.
      * Outputs:
      * * 0: A 2-D {@link OperandType::TENSOR_INT32} tensor with shape
      *      [batches, samples], containing the drawn samples.
diff --git a/neuralnetworks/1.3/types.hal b/neuralnetworks/1.3/types.hal
index 25ec915..56930c2 100644
--- a/neuralnetworks/1.3/types.hal
+++ b/neuralnetworks/1.3/types.hal
@@ -833,7 +833,7 @@
     HASHTABLE_LOOKUP = @1.2::OperationType:HASHTABLE_LOOKUP,
 
     /**
-     * Applies L2 normalization along the depth dimension.
+     * Applies L2 normalization along the axis dimension.
      *
      * The values in the output tensor are computed as:
      *
@@ -841,8 +841,7 @@
      *         input[batch, row, col, channel] /
      *         sqrt(sum_{c} pow(input[batch, row, col, c], 2))
      *
-     * For input tensor with rank less than 4, independently normalizes each
-     * 1-D slice along dimension dim.
+     * By default the axis dimension is the last dimension of the input tensor.
      *
      * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
@@ -867,6 +866,10 @@
      *      the scale must be 1.f / 128 and the zeroPoint must be 128.
      *      For {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
      *      the scale must be 1.f / 128 and the zeroPoint must be 0.
+     *
+     *      NOTE: Before HAL version 1.3, if the elements along an axis are all zeros,
+     *      the result is undefined. Since HAL version 1.3, if the elements along an axis
+     *      are all zeros, the result is logical zero.
      */
     L2_NORMALIZATION = @1.2::OperationType:L2_NORMALIZATION,
 
@@ -4063,7 +4066,8 @@
      * * 1: A scalar {@link OperandType::INT32}, specifying the number of
      *      independent samples to draw for each row slice.
      * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [2],
-     *      specifying seeds used to initialize the random distribution.
+     *      specifying seeds used to initialize the random distribution. If both
+     *      provided seeds are 0, both will be randomly generated.
      * Outputs:
      * * 0: A 2-D {@link OperandType::TENSOR_INT32} tensor with shape
      *      [batches, samples], containing the drawn samples.