Performance Optimization of RISC-V Based Embedded Systems for Real-Time AI Applications - Ethan J Walker; Hiroshi Tanaka (Computational Intelligence Systems, 2026)
Real-time artificial intelligence at the edge increasingly depends on low-power embedded pro- cessors that can deliver bounded latency under strict energy and memory constraints. RISC-V is particularly attractive in this context because its open instruction set architecture permits domain-specific extensions, fine-grained microarchitectural tuning, and portable software stacks across vendors. This paper presents a complete optimization study for a RISC-V based embed- ded AI platform targeting low-latency visual and sensor inference. We propose a hardware– software co-design pipeline that combines quantization-aware deployment, scratchpad-aware memory scheduling, vectorized kernel fusion, interrupt-aware real-time scheduling, and a lightweight custom instruction extension for convolution accumulation. The target system integrates a 64- bit dual-core RISC-V processor with RVV 1.0 support, a configurable tensor accelerator, DMA- backed scratchpad memory, and a deterministic inference runtime. Experiments are conducted on three representative workloads: keyword spotting, human activity recognition, and compact object detection. We compare the proposed design against an unoptimized RISC-V baseline, an ARM Cortex-A55 embedded reference, and software-only optimization variants. Across workloads, the proposed system reduces end-to-end latency by 38.7% relative to the optimized software-only RISC-V baseline and by 56.4% relative to the unoptimized deployment, while improving energy efficiency by up to 1.84× and preserving task accuracy within 0.6 percentage points. An ablation study demonstrates that memory scheduling and vector-kernel fusion are the dominant contributors to performance, while the custom instruction path is most beneficial for convolution-heavy models. The results indicate that carefully co-designed RISC-V platforms can satisfy practical real-time AI constraints without sacrificing flexibility or openness.