Build fast, trade fast: FPGA-based high-frequency trading using high-level synthesis
Bibliographic record
Abstract
High-Frequency Trading (HFT) systems require extremely low latency in response to market updates. This motivates the use of Field-Programmable Gate Arrays (FPGAs) to accelerate different system components such as the network stack, financial protocol parsing, order book handling and even custom trading algorithms. However, the long cycle of developing and verifying FPGA designs makes it challenging for HFT software developers to deploy such highly-dynamic systems, especially with their limited hardware design expertise. We present a complete highly-optimized infrastructure that implements low-latency system components in C++ using High-Level Synthesis (HLS). We also develop a framework that enables HFT algorithm developers to implement their trading algorithms in a high-level programming language and rapidly integrate it to the rest of the system. We implemented our HLS-based system on a Xilinx Kintex Ultrascale FPGA running at 156 MHz. Our on-board measurements show an end-to-end round-trip latency less than 870ns, which is comparable to that achieved by prior RTL-based implementations but requires reduced system development time and effort.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".