Evaluating the FPGA Selection Process in Military System Design

As technologies such as digital signal processors (DSPs), field programmable gate arrays (FPGAs), and generalpurpose processors (GPPs) advance, product and feature differentiation has become more difficult. As a result, designers must use rigorous approaches to evaluate technologies available from different military technology providers.

Evaluating Competing Criteria

Developing a system design for government projects typically requires defense contractors to evaluate and make system decisions based on documents such as a request for proposal (RFP), statement of work (SOW), and concept of operations (CONOP) (see Figure 1). From these documents, contractors assess system alternatives while maximizing the customer’s expected system goals, objectives, and capabilities. As there may be competing goals and objectives, a structured method of evaluating alternatives is required to support design decisions.

The analytical hierarchical process (AHP) is a commonly used multi-criteria decision analysis (MCDA) method used to evaluate design alternatives. AHP is favored over other MCDA methods due to its structured mathematical approach and ease of use.

This article summarizes the Analytical Hierarchical Process (AHP) and a weighted sum of products scoring approach. As with any MCDA approach, the evaluator should be careful to understand the biases of any approach on their decisions.

FPGA Trade Study Approach

An FPGA selection trade study typically focuses on mission key performance parameters (KPPs). Establishing traceability of these key performance parameters as they apply to the FPGA functionality is the first step in this process. The example criteria presented may not be applicable to every project, but many can be used to identify which FPGA vendor best meets mission objectives.

Device Availability — Developers must be confident that production devices are available when needed to support critical proposal milestones. Additionally, due to the long support and production lifecycles of military systems, FPGA product lifetimes must also be considered.

Logic Density and Efficiency — FPGA implementation efficiency can vary widely based on device architecture and algorithm design parameters. Logic utilization metrics from previous designs are the best source of data; however, metrics based on prototyping efforts may be necessary for new designs.