The goal of the H2020 SELENE project is the development of a flexible computing platform for autonomous applications that includes built-in hardware support for safety. The SELENE computing platform is an open-source RISC-V heterogeneous multicore system-on-chip (SoC) that includes 6 NOEL-V RISC-V cores and artificial intelligence accelerators. In this paper, we describe the approach followed in the SELENE project to accelerate neural network inference processes. Our intermediate results show that both the FPGA and ASIC accel-erators provide real-time inference performance for the analyzed network models at a reasonable implementation cost.

Existing HW/SW platforms for safety-critical systems suffer from limited performance and/or from lack of flexibility due to building on specific proprietary components. This jeopardizes their wide deployment across domains. While some research has been done to overcome these limitations, they have had limited success owing to missing flexibility and extensibility. Flexibility and extensibility are the cornerstones of industry adoption: industries dealing in capital goods need technologies on which they can rely on during decades (e.g. avionics, space, automotive). SELENE aims at covering this gap by proposing a new family of safety-critical computing platforms, which builds upon open source components such as the RISC-V instruction set architecture, GNU/Linux, and the Jailhouse hypervisor. SELENE will develop an advanced computing platform that is able to: (1) adapt the system to the specific requirements of different application domains, to changing environmental conditions, and to internal conditions of the system itself; (2) allow the integration of applications of different criticalities and performance demands in the same platform, guaranteeing functional and temporal isolation properties; (3) achieve flexible diverse redundancy by exploiting the inherent redundant capabilities of the multicore; and (4) efficiently execute compute-intensive applications by means of specific accelerators.