MultiVic: A Time-Predictable RISC-V Multi-Core Processor Optimized for Neural Network Inference

By Maximilian Kirschner ^1,2, Konstantin Dudzik ^1,2, Ben Krusekamp ^1,2, and Jürgen  Becker ^1,2
 1 FZI Research Center for Information Technology, Karlsruhe, Germany
 ² Karlsruhe Institute for Information Technology, Karlsruhe, Germany

Abstract

Real-time systems, particularly those used in domains like automated driving, are increasingly adopting neural networks. From this trend arises the need for high-performance hardware exhibiting predictable timing behavior. While state-of-the-art real-time hardware often suffers from limited memory and compute resources, modern AI accelerators typically lack the crucial predictability due to memory interference.

We present a new hardware architecture to bridge this gap between performance and predictability. The architecture features a multi-core vector processor with predictable cores, each equipped with local scratchpad memories. A central management core orchestrates access to shared external memory following a statically determined schedule.

To evaluate the proposed hardware architecture, we analyze different variants of our parameterized design. We compare these variants to a baseline architecture consisting of a single-core vector processor with large vector registers. We find that configurations with a larger number of smaller cores achieve better performance due to increased effective memory bandwidth and higher clock frequencies. Crucially for real-time systems, execution time fluctuation remains very low, demonstrating the platform's time predictability.

To read the full article, click here