Demonstrators
Seven demonstrators validate the TURANDOT RISC-V processors, accelerators, IP blocks and software in realistic automotive environments.
Demonstrators are the cornerstone for validating the integration and maturity of the technologies developed in TURANDOT – RISC-V processors, AI/ML accelerators, IP blocks, software toolchains and system architectures. They showcase interoperability, safety, performance and scalability, and give OEMs and Tier-1 suppliers early hands-on feedback opportunities.
D1Automotive SoC & AI Platform
RISC-V test chips integrating a high-performance real-time processor for control-oriented automotive workloads (18 nm), a low-power processor focused on automotive security, and AI acceleration.
D2Dependable AI-Enabled RISC-V MCU Platform
A scalable RISC-V MCU architecture for automotive edge computing, with a strong emphasis on functional safety, low-latency real-time performance and AI-based anomaly detection.
D3Automotive Zonal RISC-V Microcontroller and Microprocessor System
A next-generation multi-SoC RISC-V computing platform for demanding real-time workloads in zonal compute nodes, meeting ASIL safety requirements, with a high-performance SoC in 16 nm.
D4Secure RISC-V ASIC Platform
A secure ASIC based on an enhanced version of the open-source CVA6 RISC-V processor for high-assurance embedded applications, delivered as an FPGA prototype and a final 28 nm ASIC.
D5ADAS AI Application Demonstrator
Pre-development of a European RISC-V application core optimised for ADAS and in-vehicle infotainment workloads, evaluated through FPGA emulation.
D6Automotive Scalable and Configurable RISC-V Sensor Fusion Platform
A novel RISC-V-based SoC architecture for intelligent edge nodes in the car that pre-process sensor data, execute local control tasks and make fast decisions at the point of data origin.
D7 SAFE AI Platform for Real-Time Vehicle Safety and Emergency Management
An OEM-driven automotive use case for real-time safety monitoring, anomaly detection, emergency management, and event recording in future software-defined vehicles.