Improving image analysis and processing performance on the RISC-V platform with Lichee Pi 4A

N. I. Cherepanov, N. O. Stepina, I. V. Nikiforov

Peter the Great St. Petersburg Polytechnic University

Abstract: The study explores optimization methods for improving image processing performance on the RISC V platform with Lichee Pi 4A. The research focuses on real-time video processing within a microservice-based self-service system. Several existing optimization strategies are considered and evaluated, including neural network model optimization, hardware acceleration using RVV vector instructions and leveraging the built-in Neural Processing Unit (NPU). The profiling results on existing strategies indicate that object detection and feature extraction consume the most computation resources. In order to eliminate the performance gap, the model quantization to INT8 format is implemented, that allows to reduce memory usage and inference latency. Additionally, a modified ONNX Runtime version is deployed to support NPU acceleration. These improvements led to 75% reduction in model size and a 35% decrease in inference latency. The study concludes that hardware-aware optimizations significantly enchase performance on the RISC-V (Lichee Pi 4A) platform. The main issue encountered is the low processing speed on Lichee Pi 4A, with a current frame rate of only 0.05 FPS, which in unsuitable for practical usage.

Keywords: RISC-V; Lichee Pi 4A; image processing; neural network; vectorization; NPU; ONNX Runtime; performance optimization; real-time processing.

Language: English

DOI: 10.15514/ISPRAS-2025-37(5)-12