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The Exostiv Blade is designed for advanced FPGA debugging and validation, offering deep, high-speed data capture capabilities across multiple FPGAs. It supports up to 800 MHz sampling speeds and immense data bandwidth of up to 4.5 Tbps. Its architecture allows data capture from multiple FPGA nodes, with up to 5.12 TB of trace storage in a single unit. This system suits scenarios requiring extensive node visibility and is scalable, adaptable to numerous FPGA prototyping systems, providing remote access capabilities for comprehensive data analysis across diverse environments. The Blade can effectively manage pre-silicon and complex multi-FPGA systems testing, offering insights from millions of capture nodes using advanced triggering and data qualification methods.
Exostiv is engineered for massive data capture within FPGA systems, allowing monitoring and visualization of internal signals in real-time. It supports probes with up to 65 Gbps bandwidth and high-speed transceiver connections. A robust tool for FPGA design environments, Exostiv is adaptable to any prototyping board and significantly reduces the risk of FPGA bugs reaching production. With its vibrant software environment, Exostiv allows flexible manipulation of capture data, integrates Exostiv Core Inserter for IP management, and helps engineers flawlessly assess design authenticity in real-world fences.
The EXOSTIV IP suite is tailored for flexible data capture scenarios within FPGA systems, catering to different performance needs. This product facilitates observation and control over a multitude of data nodes, leveraging efficient sampling and triggering capabilities. With the capacity to capture data up to 800 MHz, EXOSTIV IP offers both Standard and Extended Width options, allowing users to balance between wide node coverage and FPGA resource usage. These IP cores can be integrated across multiple FPGAs, each core utilizing 1-4 transceivers for efficient data transport. The standard IP supports high-speed operation, while the extended version offers minimized resource footprint, enabling extensive data analysis and debugging capabilities.
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