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diff --git a/docs/user_guide/conv2d_heuristic.dox b/docs/user_guide/conv2d_heuristic.dox new file mode 100644 index 0000000000..edd24a3d36 --- /dev/null +++ b/docs/user_guide/conv2d_heuristic.dox @@ -0,0 +1,89 @@ +/// +/// Copyright (c) 2023 Arm Limited. +/// +/// SPDX-License-Identifier: MIT +/// +/// Permission is hereby granted, free of charge, to any person obtaining a copy +/// of this software and associated documentation files (the "Software"), to +/// deal in the Software without restriction, including without limitation the +/// rights to use, copy, modify, merge, publish, distribute, sublicense, and/or +/// sell copies of the Software, and to permit persons to whom the Software is +/// furnished to do so, subject to the following conditions: +/// +/// The above copyright notice and this permission notice shall be included in all +/// copies or substantial portions of the Software. +/// +/// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +/// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +/// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +/// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +/// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +/// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +/// SOFTWARE. +/// + +namespace arm_compute +{ +/** +@page conv2d_heuristic Convolution 2D heuristic + +@section conv2d_heuristic_algorithms_used Convolution 2D heuristic: algorithm selection + +The convolution 2D (in short, conv2D) is certainly one of the most compute intensive and performance critical operators in ML workloads. +This operator can be implemented with different algorithms, which differ in terms of accuracy, kernel size support, and additional memory required. +Unfortunately, it does not exist a single algorithm that can be used in all scenarios to achieve the best performance. +Therefore, the Arm Compute Library integrates an heuristic within the conv2d operators to select the most efficient algorithm, depending on input and kernel shapes and desired level of accuracy. +The heuristic depends on the target backend (either NEON™ for Arm® CPUs or OpenCL for Arm® GPUs) and the following subsections will provide the main details behind the selection of the algorithm. + +⚠ Attention: The heuristics presented in the following subsections will only refer to the NHWC data layout, which is the optimal and recommended layout for the Arm Compute Library. + +@subsection conv2d_heuristic_on_cpu Convolution 2D heuristic: Arm® Cortex®-based CPUs + +The conv2d heuristic for Arm® Cortex®-based CPUs is inside the get_convolution_method() method in the CpuConv2d function. +The algorithms used in the get_convolution_method() function are the following: +- Direct-Conv2D +- Im2Col+GeMM-based +- Indirect-GeMM (a.k.a. GEMMCONV2D) +- GeMM +- Winograd + +⚠ Attention: Winograd only works with floating-point data types (F32, F16) + +The heuristic first checks less frequent cases that we may have in ML workloads for edge devices. These cases are the following: +-# Non unit dilation: We call Im2Col+GeMM +-# Large input and kernel shapes: We call Direct-Conv2D because it is the only algorithm that does not extra additionally temporary memory +-# Small Input-Feature-Maps (IFM): In this scenario, we have found that the GeMM implementation is generally the most efficient algorithm compared to Winograd and Indirect-GeMM + +If we have a most frequent case, such as unit dilations, of larger IFM, we evaluate the following conditions instead: +-# Unit kernel size (1x1): In this scenario, the conv2d operations corresponds to a matrix multiplication and we call GeMM. +-# Winograd. Winograd only works with unit strides and supports a limited number of kernel sizes, such as 3x3, 3x1, 1x3, 5x1, 1x5 and 5x5 +-# Indirect-GeMM: It should be used in all cases expect when the kernel size is 1x1 or when the IFM is small + +If the preceding cases are not met, we will fall-back to the Im2Col+GeMM-based algorithm. + +@subsection conv2d_heuristic_on_gpu Convolution 2D heuristic: Arm® Mali™-based GPUs + +The conv2d heuristic for Arm® Mali™-based GPUs is inside the get_convolution_method() method in the ClConv2d function. + +The algorithms used in the get_convolution_method() function are the following: +- Direct-Conv2D +- Im2Col+GeMM-based +- Indirect-GeMM +- GeMM +- Winograd + +⚠ Attention: Winograd only works with floating-point data types (F32, F16) + +The heuristic first checks less frequent cases that we may have in ML workloads for edge devices. These cases are the following: +-# Non unit dilation: We call Im2Col+GeMM +-# Large input and kernel shapes: We call Direct-Conv2D because it is the only algorithm that does not extra additionally temporary memory + +In all the other cases, the GPU heuristic evaluates the suitability of Winograd and Direct-Conv2D/Indirect-Conv2D. +In particular, Winograd is adopted when the convolution parameters (kernel size and strides) are supported by the algorithm and when the IFM is not small (for example, greater than 8). +The conditions for using the Direct-Conv2D algorithms are several and we recommend you look at the heuristic directly. +In general, the Direct-Conv2D operators is used in almost all cases where kernel size is not 1x1. +The Indirect-GeMM algorithm is used in alternative to Direct-Conv2D only for Arm® Mali™-G77 GPU. +If neither Winograd nor Direct-Conv2D can be used, we will fall-back to either GeMM (when the kernel size is 1x1) or the Im2Col+GeMM-based algorithm. + +*/ +} // namespace |