A High-Level Route to AWS Cloud FPGAs, With Debugging Lessons and a Performance Bottleneck

Getting a machine-learning model onto a cloud FPGA is not the same as making it fast—or proving every stage correct. A new preprint describes a high-level development route for AWS F2 instances while documenting the numerical checks, circuit-timing problems and memory limitations encountered along the way.

A deployment workflow rather than a speed breakthrough

In the preprint Enabling AWS Cloud FPGA Development Using High-Level Vitis HLS, David Rostcheck presents a route from C++-based model code to a deployable Amazon FPGA Image, or AFI. The contribution is primarily a development and debugging workflow, not a demonstration of competitive transformer acceleration.

An FPGA implements computations as configurable circuits. High-level synthesis translates C++ into hardware-description code, but further tooling must integrate that design with the cloud platform’s infrastructure and produce an image that can be loaded onto the device.

Rostcheck connects AMD’s Vitis HLS tooling to the AWS F2 Hardware Development Kit through an existing community bridge. The workflow includes hardware export, bridge conversion, required patches, circuit implementation and AFI creation. The paper describes a month-long project conducted by one researcher using coding and desktop AI assistants; it is not a controlled evaluation of agent productivity.

Four checkpoints for finding errors

The validation method compares four implementations: an FP32 PyTorch baseline, a quantized Python reference reproducing the intended fixed-point arithmetic, HLS C-simulation using the kernel code, and execution on the physical FPGA.

The Python intermediate helps distinguish numerical compromises from implementation failures. Differences between the floating-point and quantized references expose quantization effects. Differences between the quantized reference and later implementations instead point toward code or hardware problems.

One reported incident illustrates why simulation alone is insufficient. A lookup-table initialization file was not copied into the synthesis directory. Vivado silently filled the resulting ROM with zeros, so hardware returned zero for every table input despite correct C-simulation behavior. Comparing validation levels helped localize the failure.

TinyBERT as a test case

The test model is TinyBERT, with four encoder layers, a hidden dimension of 312 and approximately 14.5 million parameters. Rostcheck fine-tuned it to identify street, city, state, ZIP and person entities in synthetic address examples. The dataset contains 560 training samples, 120 validation samples and 500 test samples.

The implementation uses 16-bit integer weights and activations, termed W16A16. The author chose this precision to reduce intermediate numerical errors that could otherwise obscure hardware defects, rather than to minimize storage or maximize throughput.

On the test set, the paper reports F1 scores of 86.63 for the FP32 baseline and 86.54 for the W16A16 Python reference. These close reported values support the choice as a debugging reference for this task, but the small synthetic dataset does not establish broader model quality.

The deployment boundary is important. The full model is implemented and validated in C-simulation, not on the FPGA. Hardware deployment covers the embedding layer and first encoder layer. The embedding stage is reported as validated across 500 samples; the first encoder has achieved timing closure, but a deterministic attention-computation discrepancy remains under investigation. Full-pipeline simulation results therefore must not be read as full-pipeline hardware validation.

Memory access overwhelms computation

The community bridge imposes substantial restrictions: one memory-master interface, a fixed 512-bit data path and no access to the device’s high-bandwidth memory through the provided template. Weights are streamed through the DDR path, while intermediate activations also return to DDR between stages.

For the embedding layer plus first encoder, the reported median batch-one latency on a 16-token input is 344.7 milliseconds on the FPGA, compared with 0.72 milliseconds on the CPU and 0.84 milliseconds on the GPU. The author attributes the approximately 480-fold and 410-fold slowdowns mainly to fetching weights through this memory path. These measurements concern only the deployed portion, not the complete model.

Circuit timing introduced additional obstacles. The device spans three physical regions, and routing between them complicated placement of memory-heavy operations. The paper documents selective memory-placement changes, lookup-table alternatives for nonlinear functions and removal of debug writes. Implementation builds took 38–82 minutes, making each experiment consequential.

What remains open

Rostcheck proposes extending the bridge with additional memory channels and high-bandwidth-memory access, or moving to lower-level hardware-description code. These are future directions, not demonstrated performance fixes.

The source code, validation infrastructure and deployment scripts are reported as publicly available, with the first three validation levels runnable locally. The practical result is a documented route into cloud FPGA development, accompanied by explicit evidence that successful deployment, numerical correctness and useful performance are separate milestones.