10 citations · 53 across the 16 of their papers we have counts for
16 papers
Energy Efficient LSTM Accelerators for Embedded FPGAs through Parameterised Architecture Design
Chao Qian, Tianheng Ling, Gregor Schiele
Long Short-term Memory Networks (LSTMs) are a vital Deep Learning technique suitable for performing on-device time series analysis on local sensor data streams of embedded devices.…
StrikeWatch: Wrist-worn Gait Recognition with Compact Time-series Models on Low-power FPGAs
Tianheng Ling, Chao Qian, Peter Zdankin +2
Running offers substantial health benefits, but improper gait patterns can lead to injuries, particularly without expert feedback. While prior gait analysis systems based on camera…
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…
Automating Versatile Time-Series Analysis with Tiny Transformers on Embedded FPGAs
Tianheng Ling, Chao Qian, Lukas Johannes Haßler +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…
Leveraging Application-Specific Knowledge for Energy-Efficient Deep Learning Accelerators on Resource-Constrained FPGAs
Chao Qian
The growing adoption of Deep Learning (DL) applications in the Internet of Things has increased the demand for energy-efficient accelerators. Field Programmable Gate Arrays (FPGAs)…
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…
ElasticAI: Creating and Deploying Energy-Efficient Deep Learning Accelerator for Pervasive Computing
Chao Qian, Tianheng Ling, Gregor Schiele
Deploying Deep Learning (DL) on embedded end devices is a scorching trend in pervasive computing. Since most Microcontrollers on embedded devices have limited computing power, it i…
An Automated Approach to Collecting and Labeling Time Series Data for Event Detection Using Elastic Node Hardware
Tianheng Ling, Islam Mansour, Chao Qian +1
Recent advancements in IoT technologies have underscored the importance of using sensor data to understand environmental contexts effectively. This paper introduces a novel embedde…
Idle is the New Sleep: Configuration-Aware Alternative to Powering Off FPGA-Based DL Accelerators During Inactivity
Chao Qian, Christopher Cichiwskyj, Tianheng Ling +1
In the rapidly evolving Internet of Things (IoT) domain, we concentrate on enhancing energy efficiency in Deep Learning accelerators on FPGA-based heterogeneous platforms, aligning…
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…
Towards Auto-Building of Embedded FPGA-based Soft Sensors for Wastewater Flow Estimation
Tianheng Ling, Chao Qian, Gregor Schiele
Executing flow estimation using Deep Learning (DL)-based soft sensors on resource-limited IoT devices has demonstrated promise in terms of reliability and energy efficiency. Howeve…
FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization
Tianheng Ling, Julian Hoever, Chao Qian +1
In industrial and environmental monitoring, achieving real-time and precise fluid flow measurement remains a critical challenge. This study applies linear quantization in FPGA-base…
Enhancing Energy-efficiency by Solving the Throughput Bottleneck of LSTM Cells for Embedded FPGAs
Chao Qian, Tianheng Ling, Gregor Schiele
To process sensor data in the Internet of Things(IoTs), embedded deep learning for 1-dimensional data is an important technique. In the past, CNNs were frequently used because they…
On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level Sensor Data
Tianheng Ling, Chao Qian, Gregor Schiele
Soft sensors are crucial in bridging autonomous systems' physical and digital realms, enhancing sensor fusion and perception. Instead of deploying soft sensors on the Cloud, this s…
A Study of Quantisation-aware Training on Time Series Transformer Models for Resource-constrained FPGAs
Tianheng Ling, Chao Qian, Lukas Einhaus +1
This study explores the quantisation-aware training (QAT) on time series Transformer models. We propose a novel adaptive quantisation scheme that dynamically selects between symmet…