Special Issue "Hardware-Aware Deep Learning"
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 May 2023 | Viewed by 13027
Special Issue Editors
Interests: hardware-aware deep learning; in-memory computing; emerging post-CMOS non-volatile memory; trustworthy AI
Interests: neuromorphic computing; secure and efficient deep learning; electronic design automation
Interests: computer vision; artificial intelligence; deep learning; image analysis and processing; visual saliency; biomedical engineering
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Special Issue Information
One of the main factors that contributes to the success of deep learning (DL) is the mighty computing power provided by modern hardware, spanning from high-performance server systems to resource-limited edge devices. The edge side (e.g., embedded systems, IoT) demands not only extreme energy-efficiency but also real-time inference capability, which requires cross-stack techniques, including model compression, compilation, architecture and circuit design of AI chips, emerging devices, etc. Beyond that, recent investigations, such federated learning, also bring model training to the edge side, with the data-security and computing limitations of mobile devices taken into consideration. On the cloud side, as the DL model size grows exponentially in the last two years (e.g., OpenAI GPT3, Google switch-transformer, etc.), how to efficiently support the training and inference of those immerse models is also an emerging research direction. Without lowering their hardware cost, however, incorporating them into the paradigm of machine learning as a service (MLaaS) will be infeasible. Moreover, the security and fault tolerant capability of DL also leads to a coherent of research, such as DL against error and non-ideal effects of the target hardware (e.g., bit-error of memory system). Therefore, the aforementioned concerns motivate the research of hardware-aware deep learning, for optimized energy, latency, and even security.
Dr. Deliang Fan
Dr. Zhezhi He
Dr. Alessandro Bruno
Manuscript Submission Information
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- acceleration of deep learning
- artificial intelligence of things (AIoT)
- model compression
- algorithm and hardware co-design for deep learning
- neural architecture search
- security issues associated with deep learning on hardware
- near-sensor intelligence
- hardware-aware compilation techniques of deep learning
- federated learning and split learning