The need for long-context reasoning has led to alternative neural network architectures besides Transformers and self-attention, a popular model being Hyena, which employs causal 1D-convolutions implemented with FFTs. Long convolutions enable efficient global context mixing, but requirements for intermediate results exceed the 2-3 MB Block RAM capacity of FPGAs. We present a chunked FFT convolution approach enabling 450K length sequence by 450K length filter convolutions on an Alveo U200 FPGA with 2.8 MB BRAM through chunking and overlap-add reconstruction. We find that throughput scales proportionally with chunk size while degrading minimally by 7% for our longest sequences, demonstrating that careful memory management enables deployment of long-context primitives on edge FPGAs without sacrificing performance.
@article{arxiv.2601.06065,
title = {Enabling Long FFT Convolutions on Memory-Constrained FPGAs via Chunking},
author = {Peter Wang and Neelesh Gupta and Viktor Prasanna},
journal= {arXiv preprint arXiv:2601.06065},
year = {2026}
}