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.
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