FPGAs in Trading: When Hardware Acceleration Makes Sense

Field-programmable gate arrays have become a staple of high-frequency trading infrastructure, but the technology is often oversold. Vendors promise nanosecond-scale improvements and imply that any firm not using FPGAs is leaving performance on the table. The reality is more nuanced.

FPGAs excel in a specific set of use cases: tasks that are computationally simple but latency-critical and high-throughput. Market data parsing is the canonical example. An FPGA can decode a binary exchange feed, normalize it, and publish the result to shared memory in under 100 nanoseconds — roughly 10x faster than optimized software running on a modern CPU. Order entry is another strong case: an FPGA can construct and transmit a FIX or binary order message in the time it takes software to make a single function call.

But FPGAs are a poor fit for complex, branching logic. Strategy decision-making — with its conditional rules, statistical calculations, and dynamic state — is better suited to a CPU where the instruction set, caches, and branch predictor are designed for exactly this kind of work. Trying to implement a complex strategy in FPGA logic leads to enormous development cost, long iteration cycles, and a codebase that is nearly impossible to debug.

At NewMachine, we help clients draw this line clearly. Our FPGA engineering team delivers turnkey solutions for market data, order entry, and network timestamping — the use cases where the ROI is proven. For everything else, we optimize the software path: tuned kernels, isolated cores, huge pages, and cache-aligned data structures. The hybrid approach gives our clients the best of both worlds without the engineering overhead of a full-FPGA strategy.