AMD Open-Sources Mini-ISP for Spartan and Artix FPGAs

Developers working with FPGAs for robotics and drone applications can now access a cost-effective image signal processor without licensing proprietary IP. AMD has open-sourced Mini-ISP on GitHub, offering a streamlined alternative to expensive commercial cores. This release allows teams to integrate essential camera processing directly into their hardware designs while reducing logic gate consumption.

Streamlined ISP strips heavy denoising for Spartan and Artix FPGAs

The Mini-ISP project strips away heavy denoising and post-sharpening routines that typically consume significant resources in standard implementations. By focusing on core functionality, the tool delivers up to 2 Gigapixels per second of throughput. It processes one, two, or four pixels per clock cycle at speeds reaching 500 MHz. This architecture enables sub-millisecond pipeline latency for real-time vision tasks.

Mini-ISP Specifications

• Throughput: up to 2 Gigapixels per second
• Clock Speed: up to 500 MHz
• Pipeline Latency: sub-millisecond
• Target Hardware: Spartan UltraScale+ or Artix FPGAs
• Functionality: black-level calibration, Bayer demosaicing, white balancing, global tone mapping

Mini-ISP performs black-level calibration, Bayer demosaicing to reconstruct full RGB color, white balancing, and global tone mapping. The design targets developers deploying cost-effective Spartan UltraScale+ or Artix FPGAs. These specific FPGA families provide the necessary hardware foundation for the open-source code. The omission of complex noise reduction algorithms keeps the logic footprint small and efficient.

This open-source release directly addresses the high cost of proprietary ISP IP for embedded vision projects. Developers can now implement essential image processing features without the overhead of commercial licensing fees. The tool is ready for immediate integration into Spartan UltraScale+ and Artix-based systems. Teams building autonomous robots or drones gain a lightweight, high-speed solution for raw camera data.

Source: NotebookCheck