Chunked FFT Convolution Extends FPGA Sequence Lengths to 450K

Global: Chunked FFT Convolution Extends FPGA Sequence Lengths to 450K

Researchers from the University of California, Los Angeles have demonstrated a method that allows field‑programmable gate arrays (FPGAs) with limited on‑chip memory to process convolutional sequences up to 450,000 elements long, a scale previously unattainable on such devices.

Background and Motivation

Long‑context reasoning in machine learning often relies on architectures that can capture extensive temporal dependencies. While Transformers dominate the field, alternative models such as Hyena employ causal one‑dimensional convolutions implemented via fast Fourier transforms (FFTs) to achieve efficient global context mixing.

Technical Approach

The authors—Peter Wang, Neelesh Gupta, and Viktor Prasanna—proposed a chunked FFT convolution technique that partitions the input signal and filter into manageable blocks, applies FFT‑based convolution to each block, and then reconstructs the full result using an overlap‑add scheme. This approach was evaluated on an Xilinx Alveo U200 accelerator that provides only 2.8 MB of block RAM.

Performance Results

Experimental results indicate that the method can handle a 450 K‑sample input convolved with a 450 K‑sample filter on the target FPGA. Throughput was observed to increase proportionally with chunk size, and the longest tested sequences suffered a modest 7 % reduction in performance relative to an ideal, unchunked implementation.

Scalability and Memory Management

By carefully managing on‑chip memory and leveraging the overlap‑add reconstruction, the technique circumvents the 2–3 MB block RAM limitation that typically restricts FPGA‑based convolutional workloads. The authors note that larger chunk sizes further improve efficiency, provided that the intermediate data fit within the available BRAM.

Potential Impact

Enabling long‑context convolutions on edge‑oriented FPGAs could broaden the deployment of advanced neural primitives in low‑latency, power‑constrained environments, such as autonomous vehicles, IoT gateways, and real‑time signal‑processing systems.

Future Directions

The paper suggests that extending the chunking strategy to other FFT‑based operations and exploring adaptive chunk sizing could yield additional performance gains and applicability across a wider range of hardware platforms.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.