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Cloud-Based Virtual Framework for Automotive Microcontroller Evaluation
Infineon introduces a digital infrastructure solution enabling automotive engineers to test and validate microcontroller capabilities without requiring physical hardware.
www.infineon.com
Infineon’s new virtual platform will simplify and accelerate the evaluation of microcontrollers for future automotive systems.
Infineon Technologies AG is releasing a cloud-based virtual platform powered by Amazon Web Services to accelerate the evaluation of microcontrollers for automotive systems. This digital infrastructure eliminates physical hardware dependencies, addressing the engineering bottlenecks associated with designing software-defined vehicles and embedded automotive architectures within the digital supply chain.
Automotive Data Ecosystem and Evaluation Bottlenecks
The automotive data ecosystem increasingly relies on software-defined vehicles, which require rapid iteration of embedded systems. Historically, microcontroller evaluation has depended on the procurement and setup of physical hardware, extending development cycles by several weeks. By virtualizing the evaluation process, automotive engineering teams can reduce this cycle to minutes. This approach lowers the evaluation cost per user and enables hundreds of engineers to work concurrently in isolated cloud environments, mitigating the risk of system conflicts and configuration errors.
Virtual Engineering Toolchain and RISC-V Integration
The platform is built upon the Virtual Engineering Workbench, an open-source offering from Amazon Web Services used for digital toolchains, infrastructure management, and hardware virtualization. It provides a browser-based interface that removes the need for local tool installations and ensures a consistent workflow across different operating systems. A key technical feature is the inclusion of the next-generation RISC-V instruction set architecture, allowing developers to test new microcontroller families before silicon is physically available. Furthermore, backend automation allows product teams to package and release new variants seamlessly, generating usage tracking insights to optimize future semiconductor architectures.
Operational Modes for Prototyping and Validation
The system supports distinct operational workflows to handle different stages of automotive engineering. The quick testing framework uses pre-configured reference applications for immediate validation of microcontroller parameters. For comprehensive prototyping, a secondary expert workflow provisions a full in-browser virtual machine development environment. This environment supports complete development lifecycle tasks, including code compilation, firmware flashing, system debugging, and performance analysis. This architecture enables experienced embedded developers to transition directly from initial evaluation to deep software prototyping.
Strategic Perspectives on Cloud-Native Engineering
According to Thomas Schneid, Vice President of Software, Partner and Ecosystem Management at Infineon Technologies, development speed serves as a critical competitive metric for the automotive sector, particularly regarding the advancement of software-defined vehicles. He noted that the cloud-based platform mitigates the traditional bottleneck of hardware-dependent evaluation by allowing engineering teams to access and test new microcontrollers, including upcoming RISC-V architectures, significantly earlier in the digital development pipeline.
Additional Context
This section details technical specifications and competitive benchmarking not included in the original news release.
Within the automotive engineering sector, cloud-based virtual hardware platforms operate alongside established simulation frameworks such as Arm Virtual Hardware and Synopsys Virtual Development Kits. While Arm Virtual Hardware primarily provisions virtual models of Cortex-M and Cortex-A processors for continuous integration pipelines, the Infineon system explicitly integrates early-stage RISC-V automotive microcontrollers. Traditional electronic design automation simulation tools offer highly granular, cycle-accurate silicon modeling suitable for low-level firmware development but often require specialized local infrastructure or substantial computational overhead. In contrast, cloud-native evaluation platforms focus on instruction-accurate or functionally accurate virtualization through standard web browsers, prioritizing immediate software validation and concurrent global access over cycle-accurate hardware emulation.
Edited by Aishwarya Mambet, Induportals Editor, with AI assistance.
www.infineon.com
Infineon Technologies AG is releasing a cloud-based virtual platform powered by Amazon Web Services to accelerate the evaluation of microcontrollers for automotive systems. This digital infrastructure eliminates physical hardware dependencies, addressing the engineering bottlenecks associated with designing software-defined vehicles and embedded automotive architectures within the digital supply chain.
Automotive Data Ecosystem and Evaluation Bottlenecks
The automotive data ecosystem increasingly relies on software-defined vehicles, which require rapid iteration of embedded systems. Historically, microcontroller evaluation has depended on the procurement and setup of physical hardware, extending development cycles by several weeks. By virtualizing the evaluation process, automotive engineering teams can reduce this cycle to minutes. This approach lowers the evaluation cost per user and enables hundreds of engineers to work concurrently in isolated cloud environments, mitigating the risk of system conflicts and configuration errors.
Virtual Engineering Toolchain and RISC-V Integration
The platform is built upon the Virtual Engineering Workbench, an open-source offering from Amazon Web Services used for digital toolchains, infrastructure management, and hardware virtualization. It provides a browser-based interface that removes the need for local tool installations and ensures a consistent workflow across different operating systems. A key technical feature is the inclusion of the next-generation RISC-V instruction set architecture, allowing developers to test new microcontroller families before silicon is physically available. Furthermore, backend automation allows product teams to package and release new variants seamlessly, generating usage tracking insights to optimize future semiconductor architectures.
Operational Modes for Prototyping and Validation
The system supports distinct operational workflows to handle different stages of automotive engineering. The quick testing framework uses pre-configured reference applications for immediate validation of microcontroller parameters. For comprehensive prototyping, a secondary expert workflow provisions a full in-browser virtual machine development environment. This environment supports complete development lifecycle tasks, including code compilation, firmware flashing, system debugging, and performance analysis. This architecture enables experienced embedded developers to transition directly from initial evaluation to deep software prototyping.
Strategic Perspectives on Cloud-Native Engineering
According to Thomas Schneid, Vice President of Software, Partner and Ecosystem Management at Infineon Technologies, development speed serves as a critical competitive metric for the automotive sector, particularly regarding the advancement of software-defined vehicles. He noted that the cloud-based platform mitigates the traditional bottleneck of hardware-dependent evaluation by allowing engineering teams to access and test new microcontrollers, including upcoming RISC-V architectures, significantly earlier in the digital development pipeline.
Additional Context
This section details technical specifications and competitive benchmarking not included in the original news release.
Within the automotive engineering sector, cloud-based virtual hardware platforms operate alongside established simulation frameworks such as Arm Virtual Hardware and Synopsys Virtual Development Kits. While Arm Virtual Hardware primarily provisions virtual models of Cortex-M and Cortex-A processors for continuous integration pipelines, the Infineon system explicitly integrates early-stage RISC-V automotive microcontrollers. Traditional electronic design automation simulation tools offer highly granular, cycle-accurate silicon modeling suitable for low-level firmware development but often require specialized local infrastructure or substantial computational overhead. In contrast, cloud-native evaluation platforms focus on instruction-accurate or functionally accurate virtualization through standard web browsers, prioritizing immediate software validation and concurrent global access over cycle-accurate hardware emulation.
Edited by Aishwarya Mambet, Induportals Editor, with AI assistance.
www.infineon.com
