Many application domains including automotive, industry automation, IoT, and space, require the usage of well-tailored edge devices capable of processing data from various sensor sources using AI, DSP, and classical software algorithms. Data processing at edge devices must satisfy real-time requirements while consuming as little electric energy and memory footprint as possible. Moreover, for many applications, the aspects of safety, security, and reliability are equally important as performance or electric energy consumption. Therefore, application-specific system-on-chip architectures for edge devices require high customization in terms of the most appropriate performance class of the processor core including necessary custom processor extensions, the memory architecture and capacity, and the design of a parameterizable AI accelerator architecture. The BMBF project Scale4Edge aims at enabling a comprehensive RISC-V-based ecosystem to efficiently assemble optimized edge devices.
By using the Scale4Edge ecosystem, the project partner Bosch developed a neural-network based audio event detection model. This use-case has been ported to a Pulpissimo-based SoC platform[1] using components and software of the Scale4Edge ecosystem.
