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AI in Automotive Engineering
Use MATLAB to access and preprocess fleet and vehicle data, build machine learning and predictive models, and deploy models to enterprise IT systems. With MATLAB you can access data stored in files, databases, and the cloud. It helps in exploring modeling approaches using machine learning and deep learning apps and to accelerate algorithms with parallel processing on CPUs, NVIDIA® GPUs, the cloud, and datacenter resources. Automatic conversion of machine learning models to C/C++ code and deep learning models to CUDA® code helps in deployment of trained models or networks to production IT systems, without recoding into another language.
MathWorks is an AUTOSAR Premium Member and actively participates in the development of the standard with focus on the complete application of Model-Based Design with an AUTOSAR development process. Use Simulink and AUTOSAR Blockset™ to design and simulate Classic and Adaptive AUTOSAR systems. Then use Embedded Coder® to generate AUTOSAR code in C for Classic or C++ for Adaptive. Composition authoring with System Composer, as well as, round-trip ARXML-based integrations are supported.
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Model-Based Design enables medical device engineers to introduce and integrate agile approaches into their existing software development workflows by cleanly separating the design phase from the implementation steps.
Using Model-Based Design, you can:
Model and simulate algorithms and entire systems before implementation
Verify and validate the designs thoroughly at a higher-level abstraction
Prototype design and create proofs-of-concept by automatically generating C or HDL code
Produce customizable reports that can be used as evidence of regulatory compliance
Prove your code is safe under all possible run-time conditions using static analysis
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Behavioral Analog/Mixed-Signal Design
Modeling and simulating analog/mixed-** systems in circuit-level tools is time consuming and limits the number of design alternatives engineers can evaluate. By contrast, MATLAB and Simulink provide a higher level of design abstraction and faster modeling and simulation, which enables engineers to quickly evaluate numerous design options.
Semiconductor engineers use MATLAB and Simulink models as golden references when they create transistor-level circuit models. These MATLAB and Simulink models integrate with analog/mixed-** design tools such Cadence® Virtuoso® AMS Designer, and can also be used as behavioral verification models in production SystemVerilog test environments.
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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。
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