Conventional image signal processing (ISP) control algorithms based on human visual perception are insufficient for the demands of modern machine vision systems. Although recent learning-based methods have attempted to adapt ISP hyperparameters for specific vision tasks, their high latency and hardware cost of iterative optimization hinder deployment on edge devices such as those used in autonomous driving. To address these limitations, this paper proposes a real-time machine vision system through algorithm–hardware co-design. First, we introduce a one-iteration learning framework to avoid iterative optimization, significantly reducing latency for real-time use. Second, we propose a hardware-friendly controller, RasterNet, specifically tailored for raster-scanning sensor dataflow, eliminating redundant computation. Third, we present a pipelined ISP controller architecture incorporating branch and chroma time division multiplexing techniques to minimize the number of processing elements, achieving a compact and efficient design. Experiments demonstrate that the proposed system achieves superior object detection accuracy on resource-constrained platforms, with real-time performance reaching 70 FPS on FPGA and 224 FPS on ASIC implementations.

Abstract Over the past 70 years, the semiconductor industry has undergone transformative changes, largely driven by the miniaturization of devices and the integration of innovative structures and materials. Two-dimensional (2D) materials like transition metal dichalcogenides (TMDs) and graphene are pivotal in overcoming the limitations of silicon-based technologies, offering innovative approaches in transistor design and functionality, enabling atomic-thin channel transistors and monolithic 3D integration. We review the important progress in the application of 2D materials in future information technology, focusing in particular on microelectronics and optoelectronics. We comprehensively summarize the key advancements across material production, characterization metrology, electronic devices, optoelectronic devices, and heterogeneous integration on silicon. A strategic roadmap and key challenges for the transition of 2D materials from basic research to industrial development are outlined. To facilitate such a transition, key technologies and tools dedicated to 2D materials must be developed to meet industrial standards, and the employment of AI in material growth, characterizations, and circuit design will be essential. It is time for academia to actively engage with industry to drive the next 10 years of 2D material research.