Function arm_nn_mean_f32

Function Documentation

arm_cmsis_nn_status arm_nn_mean_f32(const float32_t *input_data, const cmsis_nn_dims *input_dims, const cmsis_nn_dims *axis_dims, float32_t *output_data, const cmsis_nn_dims *output_dims)

Computes the mean of a float32 tensor along the specified axes.

Values are accumulated and divided once in float32; unlike the float16 variant there is no wider accumulator, so rounding error can grow with the reduction length, matching arm_reduce_sum_f32. Because the intermediate accumulation is itself float32, it can saturate to +/-Inf even when the mean itself is representable, but whether it does depends on accumulation order: a strictly sequential build keeps one running sum, while vector builds — MVE intrinsics, or compiler auto-vectorization of the scalar path at -Ofast — fold per-lane partial sums, so on inputs whose partial sums exceed FLT_MAX in magnitude either build may return +/-Inf and the two may disagree (one finite, one Inf); when partial sums of opposite sign both saturate, the vector fold can even yield NaN (Inf + -Inf) from all-finite inputs. Only when every accumulation order overflows — e.g. same-signed values summing past FLT_MAX — is +/-Inf guaranteed on all builds. This is the accumulation-order divergence described below taken to the extreme. NaN and Inf propagate. A mean over all -0.0f inputs returns +0.0f on every build: the accumulator starts at +0.0f and (+0.0f) + (-0.0f) is +0.0f under round-to-nearest. Vector and scalar builds may differ in final ulps because float accumulation order differs.

Unlike arm_reduce_sum_f32 (identical signature, null checks only), this kernel validates shapes and returns ARM_CMSIS_NN_ARG_ERROR when any input dimension is less than 1, when any output_dims entry differs from the input shape with the reduced axes collapsed to 1, or when the input element count or the reduction count does not fit in int32_t. output_data must not overlap input_data: each output element is written after reading its whole reduction set, so an aliased write can corrupt inputs still to be read.

Parameters:

  • input_data – [in] Pointer to input tensor

  • input_dims – [in] Input tensor dimensions (4D NHWC)

  • axis_dims – [in] 4D binary axis mask (non-zero = reduce that axis)

  • output_data – [out] Pointer to output tensor

  • output_dims – [in] Output tensor dimensions (reduced axes have size 1)

Returns:

ARM_CMSIS_NN_SUCCESS on success or ARM_CMSIS_NN_ARG_ERROR on invalid arguments.