The SELENE Deep Learning Acceleration Framework for Safety-related Applications
Laura Medina1, Salva Carrion1, Pablo Andreu1, Tomas Picornell1, Jose Flich1, Carles Hernàndez1, Michael Sandoval2, Markel Sainz2, Charles-Alexis Lefebvre2, Martin Rönnbäck3, Martin Matschnig4, Matthias Wess4 and Herbert Taucher4
1Universitat Politècnica de València (Spain)
2Ikerlan Technology Research Centre (Spain)
3Cobham Gaisler (Sweeden)
4Siemens Technology (Austria)
ABSTRACT
The goal of the H2022 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 accelerators provide real-time inference performance for the analyzed network models at a reasonable implementation cost.
Keywords: Neural Networks, real-time, Autonomy.
