Vibration Gestures on Furniture via Efficient FPGA Neural Networks
Researchers compress neural networks for gesture recognition on low-power FPGAs, eliminating complex preprocessing and cutting energy use to under 1.2 mJ per inference.
Source: arxiv/cs.AI · Koki Shibata, Tianheng Ling, Chao Qian, Tomokazu Matsui, Hirohiko Suwa, Keiichi Yasumoto, Gregor Schiele · open original ↗
Compact 1D-CNN models on FPGAs enable real-time vibration-based gesture recognition on furniture with minimal energy and preprocessing overhead.
- — Raw waveform input replaces spectral preprocessing, shrinking input size 21x without accuracy loss.
- — Two lightweight architectures (1D-CNN, 1D-SepCNN) reduce parameters from 369M to 216 while maintaining performance.
- — Integer-only quantization and automated RTL generation enable direct FPGA deployment without manual optimization.
- — Ping-pong buffering in 1D-SepCNN handles tight memory constraints on low-cost Spartan-7 FPGAs.
- — Hardware-aware search framework balances accuracy, latency, energy, and deployability constraints automatically.
- — 6-bit 1D-CNN achieves 97% accuracy with 9.22 ms latency; 8-bit variant reaches 6.83 ms (53x CPU speedup).
- — Both models consume under 1.2 mJ per inference, enabling months of continuous operation on battery.
Astrobobo tool mapping
- Knowledge Capture Record the three optimization steps (raw waveform, lightweight architecture, quantization) as a reusable checklist for future edge-ML projects. Tag with 'fpga' and 'preprocessing-elimination'.
- Focus Brief Summarize the hardware-aware search framework concept: define your constraints (latency, energy, accuracy), then automate model selection. Use this as a template for your next embedded deployment.
- Reading Queue Queue the cited prior work on vibration sensing and FPGA neural networks to understand the baseline complexity this paper eliminated.
Frequently asked
- Spectral preprocessing (FFT, filtering) requires complex on-board logic and large intermediate buffers. Raw waveform input shrinks the data 21x and eliminates preprocessing hardware, freeing FPGA resources for the neural network itself. This trade-off works because modern compact CNNs learn features directly from raw signals without sacrificing accuracy.
cite ▸
APA
Koki Shibata, Tianheng Ling, Chao Qian, Tomokazu Matsui, Hirohiko Suwa, Keiichi Yasumoto, Gregor Schiele. (2026, April 22). Vibration Gestures on Furniture via Efficient FPGA Neural Networks. Astrobobo Content Engine (rewrite of arxiv/cs.AI). https://astrobobo-content-engine.vercel.app/article/vibration-gestures-on-furniture-via-efficient-fpga-neural-networks-28d41e
MLA
Koki Shibata, Tianheng Ling, Chao Qian, Tomokazu Matsui, Hirohiko Suwa, Keiichi Yasumoto, Gregor Schiele. "Vibration Gestures on Furniture via Efficient FPGA Neural Networks." Astrobobo Content Engine, 22 Apr 2026, https://astrobobo-content-engine.vercel.app/article/vibration-gestures-on-furniture-via-efficient-fpga-neural-networks-28d41e. Based on "arxiv/cs.AI", https://arxiv.org/abs/2510.23156.
BibTeX
@misc{astrobobo_vibration-gestures-on-furniture-via-efficient-fpga-neural-networks-28d41e_2026,
author = {Koki Shibata, Tianheng Ling, Chao Qian, Tomokazu Matsui, Hirohiko Suwa, Keiichi Yasumoto, Gregor Schiele},
title = {Vibration Gestures on Furniture via Efficient FPGA Neural Networks},
year = {2026},
url = {https://astrobobo-content-engine.vercel.app/article/vibration-gestures-on-furniture-via-efficient-fpga-neural-networks-28d41e},
note = {Astrobobo rewrite of arxiv/cs.AI, https://arxiv.org/abs/2510.23156},
}