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Today’s analysis and emulation of genetic sequences demands a low-latency, high-bandwidth solution to transfer massive amounts of data between processors.
Historically, data scientists to manually extracted a genetic sequence from a biological sample, ran it through the system, waited for it to process, and returned to perform the process again.
With a low-latency, high-bandwidth design today, medtech companies can train more models with machine learning algorithms in a shorter amount of time, ultimately drastically reducing development timelines.
Using an FPGA-based platform like Fidus Sidewinder minimizes the latency associated with large-scale storage devices and results in a custom system that can transmit, receive, and store tens of gigabytes per second of genetic sequencing data when combined with a Xilinx multiprocessor systems-on-a-chip (MPSoC).
Learn about how Fidus achieved high-bandwidth, low-latency data movement in a proof of concept for a medical technology company’s genetic sequencing application – click here.
This deep dive explores how to tackle debugging challenges at the intersection of FPGA hardware and software. From clock domain crossings to distributed system issues, learn strategies, tools, and cultural best practices that reduce debug time and build more resilient embedded systems.
FPGA Co-Processors are redefining what’s possible at the edge—enabling real-time analytics with precision, efficiency, and scalability. This guide explores proven design patterns, integration models, and optimization strategies to help engineering teams build smarter, faster embedded systems.
This in-depth guide explores the evolving security challenges in FPGA-based embedded systems. Learn how to implement secure boot, protect against runtime threats, and build resilient architectures that meet the demands of aerospace, automotive, and medical applications. See how FPGAs like AMD Versal adaptive SoCs support robust security from design through deployment.
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