From aba6b576d10f1388ac99e182fad00e722225fddd Mon Sep 17 00:00:00 2001 From: liutuo Date: Tue, 26 Jun 2018 15:31:13 +0800 Subject: [PATCH] check docs --- README.md | 18 +++++++++--------- docs/development/contributing.md | 2 +- .../create_a_model_deployment.rst | 6 +++--- 3 files changed, 13 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index 21d1b123..079a1df8 100644 --- a/README.md +++ b/README.md @@ -10,31 +10,31 @@ [中文](README_zh.md) **Mobile AI Compute Engine** (or **MACE** for short) is a deep learning inference framework optimized for -mobile heterogeneous computing platforms. The design is focused on the following +mobile heterogeneous computing platforms. The design focuses on the following targets: * Performance * The runtime is highly optimized with NEON, OpenCL and Hexagon, and [Winograd algorithm](https://arxiv.org/abs/1509.09308) is introduced to - speed up the convolution operations. Except for the inference speed, the - initialization speed is also intensively optimized. + speed up the convolution operations. Besides the fast inference speed, the + initialization part is also intensively optimized to be faster. * Power consumption * Chip dependent power options like big.LITTLE scheduling, Adreno GPU hints are - included as advanced API. + included as advanced APIs. * Responsiveness - * UI responsiveness gurantee is sometimes obligatory when runing a model. + * UI responsiveness guarantee is sometimes obligatory when running a model. Mechanism like automatically breaking OpenCL kernel into small units is introduced to allow better preemption for the UI rendering task. * Memory usage and library footprint - * Graph level memory allocation optimization and buffer reuse is supported. + * Graph level memory allocation optimization and buffer reuse are supported. The core library tries to keep minium external dependencies to keep the library footprint small. * Model protection - * Model protection is one the highest priority feature from the beginning of + * Model protection is the highest priority feature from the beginning of the design. Various techniques are introduced like coverting models to C++ code and literal obfuscations. * Platform coverage * A good coverage of recent Qualcomm, MediaTek, Pinecone and other ARM based - chips. CPU runtime is also compitable with most POSIX systems and + chips. CPU runtime is also compatible with most POSIX systems and archetectures with limited performance. ## Getting Started @@ -44,7 +44,7 @@ targets: ## Performance [MACE Model Zoo](https://github.com/XiaoMi/mace-models) contains -several common neural networks models and built daily against a list of mobile +several common neural networks and models which will be built daily against a list of mobile phones. The benchmark result can be found in the CI result page. ## Communication diff --git a/docs/development/contributing.md b/docs/development/contributing.md index 5dfd9e78..8b3a02fe 100644 --- a/docs/development/contributing.md +++ b/docs/development/contributing.md @@ -4,7 +4,7 @@ Contributing guide License ------- -The source file should contains a license header. See the existing files +The source file should contain a license header. See the existing files as the example. Python coding style diff --git a/docs/getting_started/create_a_model_deployment.rst b/docs/getting_started/create_a_model_deployment.rst index 184d1101..b6cdd5a0 100644 --- a/docs/getting_started/create_a_model_deployment.rst +++ b/docs/getting_started/create_a_model_deployment.rst @@ -6,15 +6,15 @@ file. One deployment file describes a case of model deployment, each file will generate one static library (if more than one ABIs specified, -there will be one static library for each). The deployment file can contains -one or more models, for example, a smart camera application may contains face +there will be one static library for each). The deployment file can contain +one or more models, for example, a smart camera application may contain face recognition, object recognition, and voice recognition models, which can be defined in one deployment file), Example ---------- -Here is an deployment file example used by Android demo application. +Here is an example deployment file used by an Android demo application. TODO: change this example file to the demo deployment file (reuse the same file) and rename to a reasonable name. -- GitLab