10 citations · 53 across the 16 of their papers we have counts for
5 papers · 1 filter
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.…
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)…
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…
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…
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…