RTOS from Mentor targets connected embedded devices

Mentor Graphics Corp. has rolled out its Mentor Embedded Nucleus real time operating system (RTOS) aimed at high-performance, next-generation applications for connected embedded devices. According to the company, the Nucleus RTOS process model for ARM Cortex-M based cores has expanded to offer application Dynamic Linking and Loading (DLL) capabilities for devices.

Internet of Things (IoT) endpoints based on Cortex-M cores with the Nucleus RTOS and process model technology can now be reconfigured, updated and/or provisioned using cloud-based services to use remote software services. This allows embedded developers to dynamically modify application software during system operation, keeping the target up to date, even in mission-critical environments. The comprehensive, ready-to-use Nucleus RTOS solution provides the IoT middleware, scalable footprint, power management and security required for today's IoT connected devices.

The lightweight Nucleus process model takes advantage of the memory protection unit (MPU) of Cortex-M0+, Cortex-M3 and Cortex-M4 cores to create protected regions in memory that can be used for dynamic application loading and unloading at system startup or afterward during run time. This release also includes Constrained Application Protocol (CoAP) with DTLS security support, and support for Texas Instruments (TI) WiLink 8 module solution for Wi-Fi and Bluetooth wireless combo connectivity.

Nucleus process model: Dynamic linking and loading for IoT devices

The growth of smart IoT devices with feature-rich applications and the need to interact with cloud-based services are placing new requirements on embedded IoT software developers. Wearables, portable medical devices, energy management systems and other smart connected devices are routinely based on resource-limited systems using Cortex-M cores. IoT systems have limited memory, so a single monolithic application cannot be loaded at startup with all the software required for the breadth of services. IoT systems with limited memory resources must run applications based on current need, and with the ability to free memory resources when the application is no longer required. Large applications must be parsed into smaller algorithms and loaded sequentially as modules, only when needed to conserve memory resources. The management of software on IoT devices requires the ability to upgrade applications and/or load new modules using cloud- based services.

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