Zobrazeno 1 - 10
of 112
pro vyhledávání: '"Schiele Gregor"'
Publikováno v:
Current Directions in Biomedical Engineering, Vol 9, Iss 1, Pp 471-474 (2023)
This paper presents a software-based Python framework for developing future AI-enhanced end-to-end Brain-Computer-Interfaces (BCI). This framework contains modules from the emulated analogue front-end and from neural signal pre-processing for invasiv
Externí odkaz:
https://doaj.org/article/fc16ebc9802f4db68f40fc9d181431ef
This study addresses the deployment challenges of integer-only quantized Transformers on resource-constrained embedded FPGAs (Xilinx Spartan-7 XC7S15). We enhanced the flexibility of our VHDL template by introducing a selectable resource type for sto
Externí odkaz:
http://arxiv.org/abs/2410.03294
ElasticAI: Creating and Deploying Energy-Efficient Deep Learning Accelerator for Pervasive Computing
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 is necessary to add a DL accelerator. Embedded Field Programmable Gate
Externí odkaz:
http://arxiv.org/abs/2409.09044
Autor:
Ling, Tianheng, Schiele, Gregor
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 models. However, it is difficult to obtain the desired performance wh
Externí odkaz:
http://arxiv.org/abs/2408.16495
Recent advancements in IoT technologies have underscored the importance of using sensor data to understand environmental contexts effectively. This paper introduces a novel embedded system designed to autonomously label sensor data directly on IoT de
Externí odkaz:
http://arxiv.org/abs/2407.11042
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 and Quantization-Aware Training with optimized hardware designs to rea
Externí odkaz:
http://arxiv.org/abs/2407.11041
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. However, its application in the field of wastewater flow estimation remains
Externí odkaz:
http://arxiv.org/abs/2407.05102
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 with the principles of sustainable computing. Instead of focusing on
Externí odkaz:
http://arxiv.org/abs/2407.12027
In industrial and environmental monitoring, achieving real-time and precise fluid flow measurement remains a critical challenge. This study applies linear quantization in FPGA-based soft sensors for fluid flow estimation, significantly enhancing Neur
Externí odkaz:
http://arxiv.org/abs/2403.01922
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 study shift towards employing on-device soft sensors, promising heighte
Externí odkaz:
http://arxiv.org/abs/2311.15036