7 citations · 16 across the 10 of their papers we have counts for
7 papers · 1 filter
KANLib -- A Modular, Extensible and Fast Kolmogorov-Arnold Network Implementation
Julian Hoever, Gregor Schiele
Kolmogorov-Arnold Networks (KANs) have recently emerged as a promising alternative to traditional multilayer perceptrons by replacing linear weights with learnable univariate funct…
Enabling Vibration-Based Gesture Recognition on Everyday Furniture via Energy-Efficient FPGA Implementation of 1D Convolutional Networks
Koki Shibata, Tianheng Ling, Chao Qian +4
The growing demand for smart home interfaces has increased interest in non-intrusive sensing methods like vibration-based gesture recognition. While prior studies demonstrated feas…
Automated Energy-Aware Time-Series Model Deployment on Embedded FPGAs for Resilient Combined Sewer Overflow Management
Tianheng Ling, Vipin Singh, Chao Qian +2
Extreme weather events, intensified by climate change, increasingly challenge aging combined sewer systems, raising the risk of untreated wastewater overflow. Accurate forecasting…
Resource-aware Mixed-precision Quantization for Enhancing Deployability of Transformers for Time-series Forecasting on Embedded FPGAs
Tianheng Ling, Chao Qian, Gregor Schiele
This study addresses the deployment challenges of integer-only quantized Transformers on resource-constrained embedded FPGAs (Xilinx Spartan-7 XC7S15). We enhanced the flexibility…
Integer-only Quantized Transformers for Embedded FPGA-based Time-series Forecasting in AIoT
Tianheng Ling, Chao Qian, Gregor Schiele
This paper presents the design of a hardware accelerator for Transformers, optimized for on-device time-series forecasting in AIoT systems. It integrates integer-only quantization…
Automating Versatile Time-Series Analysis with Tiny Transformers on Embedded FPGAs
Tianheng Ling, Chao Qian, Lukas Johannes HaÃler +1
Transformer-based models have shown strong performance across diverse time-series tasks, but their deployment on resource-constrained devices remains challenging due to high memory…