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Reference Guide: Digital Conversion Module (DCM)

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Page 2 of 3 For a complete list of products, visit Customer Success Story: Key Customer Maximizes FPGA Performance in a SWaP-Constrained Application with Mercury Partnership The trend for newer applications to require more advanced processing capabilities in a smaller footprint continues, and customers are increasingly focusing on power management. In a recent partnership, Mercury Systems had the opportunity to collaborate on a solution for a SWaP-constrained EW system designed for an airborne application, addressing these pressures directly. A New Challenge with a Traditional Approach The customer's system was created with two FPGA-based transmit/ receive modules and had limitations that required conduction cooling and a strict weight limit. In order for the product to be successful, Mercury had to determine the optimal balance between FPGA processing capability and the weight of the cooling hardware. Mercury first took a traditional industry approach to meet the request. However, without the ability to accurately understand the power dissipation as a function of the customer algorithm, the approach involved making a variety of conservative assumptions. The outcome was a design that maximized the cooling capability of the modules but required the use of heavy copper heat exchangers, which would fail to comply with the weight limit. The developer would need to request a waiver from their customer, reducing the capability of the customer's system. To better address the weight issue, Mercury incorporated its applied modeling technique to achieve a product that operated well within the required criteria. The Novel Approach for a Timely Solution When working with challenges such as this one, Mercury uses a unique modeling tool to balance efficiency without sacrificing accuracy. The tool takes the results of one modeling activity and applies it to a subsequent design, simplifying the process of entering the various parameters into the model supplied by the FPGA device manufacturer. Then, by taking the power model as it's provided and adding automation to generate the various inputs, the desired balance is achieved without wasting time. The first step was to work with the customer to understand the algorithm that would be installed on the FPGA modules. By applying select characteristics of the customer's algorithm to the EchoCore® Power Load IP, Mercury determined the inputs to the modeling tool. After running an automated script, they then calculated the predicted power dissipation of each FPGA device. With this information, Mercury's mechanical engineering team applied standard thermal modeling techniques for a known heat source to determine how to best approach the issue. In order to sufficiently cool the devices for a given rail temperature, one module could use a lightweight aluminum heat exchanger while the other higher-powered module required a copper heat exchanger. By using the heavier, more thermally conductive material only where needed, Mercury was able to optimize both the weight and the cooling, meeting the program requirements and positioning the customer for success. Through the scalability of the power- predicting tool, Mercury narrowed the list of potential FPGA to sizes that could be sufficiently cooled using that same planned cooling approach. This knowledge allowed them to quickly provide technical guidance to the customer when they determined their application would require a larger FPGA device. Successful Methodology When compared to the traditional method of making conservative assumptions, it is obvious that accurate modeling is critical to define the architecture and achieve first-pass success. In the current environment of rapidly emerging threats, customers cannot afford to settle for sub-optimal designs with extended development lead time. Using this tool, along with a team of experts, Mercury can quickly understand the power dissipation trade-offs and work collaboratively with customers to find the best solutions. Customers cannot afford to settle for sub-optimal designs with extended development lead time.

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