使用 MATLAB 进行 FPGA、ASIC 及 SoC 开发
自动执行您的工作流程 — 从算法开发到硬件设计和验证
领域*和硬件工程师运用 MATLAB® 和 Simulink® 开发原型和生产应用程序，以部署到 FPGA、ASIC 和 SoC 设备。
在高抽象级别上使用 Simulink 进行数字、模拟和软件建模及仿真
使用自动导引转换为**运算，或为任意目标设备生成原生浮点运算
通过内存、总线和 I/O 建模分析硬件和软件架构
生成经过优化、可读且可跟踪的 VHDL® 或 Verilog®，用于数字逻辑中的实现
生成处理器优化的 C/C++ 代码，供嵌入式目标处理器使用
验证 HDL 仿真器或者连接 MATLAB 或 Simulink 测试平台的 FPGA 或 SoC 设备上运行的算法
MATLAB 和 Simulink 产品适用于各种应用，如交流电机控制、软件无线电和嵌入式视觉。
Electronics engineers rely on MATLAB® and Simulink® to develop smaller, more efficient devices that integrate audio, video, wireless communications, and other features. They use these tools to explore and validate algorithms and system-level designs in hours or days rather than weeks.
By capturing algorithmic and system models in MATLAB and Simulink products, engineers can collaborate more effectively across geographic boundaries and between Signal Processing, analog/mixed-**, and control logic design teams. With these productivity gains, companies can deliver innovative solutions faster.
Engineering teams use Model-Based Design with MATLAB® and Simulink® to verify and validate embedded systems. Teams author requirements directly in their models and can then use those models to generate production code for certification.
Author requirements in your model, and verify and trace them to the design, tests, and code.
Prove that your design meets requirements, and automatically generate tests.
Check compliance of models and code using static analysis and formal methods.
Find bugs, security vulnerabilities, and prove the absence of critical run-time errors.
Produce reports and artifacts, and certify to standards (such as DO-178 and ISO 26262).
MATLAB在数据科学中的应用：
Explore data; build machine learning models;
do predictive analytics
MATLAB® makes data science easy with tools to access and preprocess data, build machine learning and predictive models, and deploy models to enterprise IT systems.
Access data stored in flat files, databases, data historians, and cloud storage, or connect to live sources such as data acquisition hardware and financial data feeds
Manage and clean data using datatypes and preprocessing capabilities for programmatic and interactive data preparation, including apps for ground-truth labeling
Document data analysis with MATLAB graphics and the Live Editor notebook environment
Apply domain-specific feature engineering techniques for sensor, text, image, video, and other types of data
Explore a wide variety of modeling approaches using machine learning and deep learning apps
Fine-tune machine learning and deep learning models with automated feature selection and hyperparameter tuning algorithms
Deploy machine learning models to production IT systems, without recoding into another language
Automatically convert machine learning models to standalone C/C++ code
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