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| Photo: Electronic Design |
The annual release of MATLAB and Simulink from MathWorks always brings new features to the fore. That remains the case with MATLAB 2019a, which packs quite a few enhancements and additions to an already formidable development package. It has new blocksets that include support for AUTOSAR, SoCs, and mixed signals. There are two new toolboxes—one is for SerDes development and the other for reinforcement learning. Integration with Polyspace includes access to its bug finder and prover, including server support for these features. Simulink’s System Composer design and analysis tools target system and software architectures.
Machine learning (ML) isn’t hype, but it’s not a trivial task to develop, train, and deploy ML models. The Reinforcement Learning Toolbox, which helps streamline the process (Fig. 1), is just one of many enhancements in this area. The toolbox provides MATLAB functions and SIMULINK blocks for training policies using reinforcement learning algorithms such as Deep Q-Network (DQN), Advantage Actor Critic (A2C), and Deep Deterministic Policy Gradients (DDPG). The policies can be used to implement control and decision-making algorithms for applications such as robotics...
Polyspace, acquired by MathWorks some time ago, has been taking advantage of its analysis tools since then. Polyspace code prover and bug finder are designed to help developers meet and document safety standards such as MISRA, ISO 26262, IEC 61508, and DO-178, as well as FDA regulations. They’re also used to check software for security vulnerabilities to address standards such as CWE, CERT-C, and ISO/IEC 17961. Polyspace for Ada can be used to prove the absence of run-time errors in Ada source code.
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Source: Electronic Design
