The standard path from a trained neural network to an FPGA involves at least three separate tools and two handoff gaps: train a model, quantize it, synthesize the quantized representation to hardware. Bern2Edge eliminates the second handoff. By converting a pretrained teacher network to Bernstein polynomial activations through knowledge distillation, it produces a representation that directly maps to LUT-based hardware or to symbolic rules with explicit input-space constraints, bypassing the quantization-then-synthesis sequence.
On an AMD Xilinx KV260 FPGA, Bern2Edge achieves 99.8% latency reduction and 95.2% BRAM reduction versus the W8A8 quantized teacher baseline, while staying within 0.5 percentage points of teacher accuracy. The symbolic rule path adds interpretability: inference runs as explicit input-space conditionals, not a black-box LUT activation. Accepted at CODES+ISSS 2026 and TCAD 2026.
The constraint being removed is the coordination cost between the machine learning team that trains the model and the hardware team that synthesizes it. When training and synthesis share the same polynomial representation, the optimization feedback loop collapses from a multi-week iteration involving two tool chains to a single compilation pass. For teams deploying custom neural inference on FPGAs in safety-critical or resource-constrained settings, the symbolic rule path also removes the "explain this activation" problem that LUT-based implementations have never solved. Teams that currently hand off between a quantization tool chain and an FPGA synthesis tool chain will compress that to a single compilation pass. The 0.5pp accuracy floor excludes applications with tight classification margins, but the larger pattern, polynomial activations as a shared representation between training and synthesis, will propagate to other activation families. The two-tool handoff is the coordination cost that breaks next.