Overview
The logiBMP is 2.5D graphics accelerator from Xylon logicBRICKS IP library, optimized for Xilinx FPGAs and designed to speed up graphics operations with bitmaps. This IP core significantly speeds up GUI rendering. The logiBMP supports very complex bitmap operations like texture renderings, picture filtering, up and down scaling, and bitmap rotating. The core is fully embedded into Xilinx Platfrom Studio and EDK tools, and its integration with on-chip CoreConnect PLB and OPB busses is very simple. Parametrizable VHDL design allows tuning of slice consumption and features set through an easy-to-use GUI interface. The logiBMP enables perspective correct texture renderings of 2.5D graphics scenes. The IP core can be easily integrated with other logicBRICKS, i.e. logiCVC Compact Multilayered Video Controller and the logiBITBLT 2D graphics accelerator to support smooth graphics transition and animations.
Learn more about GPU IP core
Imagination GPU Driver 26.1 introduces key Vulkan advancements, including support for Android 17, enhancing performance and developer capabilities for modern graphics workloads.
Explore how the SpacemiT K3 processor combines RISC-V CPUs and GPUs to revolutionize high-performance SoCs for AI-driven applications. Discover the future of computing.
A full SoC tape-out at 5nm approaches $400M in fully loaded, non-recurring engineering and mask costs. At 3nm, estimates push past $600M. Every IP block on that die is a commitment to a set of assumptions about what the silicon will need to do. In AI, those assumptions have a shorter shelf life than they used to.
Discover why the future of edge GPU design focuses on power efficiency over area, driven by thermal constraints at sub-2nm nodes. Learn about architectural shifts and Imagination's innovative solutions.
The automotive industry is undergoing the most significant transformation since the advent of electronics in cars. Vehicles are becoming software-defined, connected, AI-driven, and continuously updated. This evolution brings extraordinary new capability – but it also brings greater levels of cybersecurity and functional-safety risks.
Scaling GPU performance across multiple cores sounds simple in theory: add more cores, get more performance. In practice, it’s one of the toughest challenges in graphics architecture. While some workloads scale well thanks to their independent nature, some workloads, especially geometry processing, introduce order dependencies that make linear performance scaling a tricky problem to solve for every GPU architecture in the industry.