High performance hardware is required for the demanding workloads associated with medical image processing. Such systems represent significant investments and they need to be supported throughout long lifecycles. Implying lifecycle management (LCM) costs are a major component in total cost of ownership (TCO).
Today’s typical hardware architectures are x86 CPUs combined with GPUs for offloading specific tasks such as image reconstruction. While meeting performance requirements and being easy-to-use, these architectures suffer from frequent component obsolescence and incompatibility issues, escalating LCM costs. Using FPGAs can be a solution.
The long availability of FPGAs is rooted in their widespread use in the embedded computing market. Like in the medical market, embedded systems typically have onger, eliminating the costs of frequent redesigns and last-time buys and reducing cost of ownership.
As an array of programmable gates, FPGAs are very flexible by nature. Both compute resources and I/O functions can be optimally handled in an FPGA. This make FPGAs a very future-proof solution, simplifying platform management and reducing time-to-market.
Finally, the ease-of-use has improved significantly in recent years. FGPA programming is done in hardware description language (HDL), which is typically seen as cumbersome. Yet, in recent years frameworks such as OpenCL, have matured and offer C-like environments that enable easy programming.
