10 citations · 33 across the 7 of their papers we have counts for
7 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.…
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…
On-device AI: Quantization-aware Training of Transformers in Time-Series
Tianheng Ling, Gregor Schiele
Artificial Intelligence (AI) models for time-series in pervasive computing keep getting larger and more complicated. The Transformer model is by far the most compelling of these AI…
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…