Embedding Machine Learning for Resource-Constrained Computing Platforms

Dear Colleagues,

With the growth of computational capability and data, machine learning (ML) applications have become ubiquitous in our personal lives (smart home, smart devices). They are also widely popular in the scientific (medical) and industrial domains. Broadly, ML models are employed either to predict behaviour or extract some meaningful insights from data. The autonomous-X* domain has been broadly employed for object detection/classification, pattern recognition, and natural language processing.

IoT devices also play an essential part in collecting context-specific data, which ML models can further process to achieve higher performance (throughput, lower latency, and scalability). At lower energy cost, application-specific accelerators and complex programming frameworks (with compiler support such as MLIR with TensorFlow) are also being researched and developed. Overall, the scientific community and industry have an increasing interest in making hardware intelligent by porting ML and DL (deep learning being a subset of ML domain) models. Unlike legacy computing equipment (e.g., servers, desktop computers), embedded systems are more severely constrained by additional factors. Such factors include limited power consumption, time-bound processing, and limited scalability. In such scenarios, ML/DL applications can be manifold: from designing embedded accelerators for ML/DL algorithms to ML/DL applications optimising the operations of a given embedded system to ML algorithms for specific scientific/industrial applications leveraging embedded systems.

This Special Issue will cover innovative, holistic approaches to the design of ML/DL algorithms focusing on the convergence between embedded systems and the ML domain. These include (but are not limited to):

• Custom accelerators for low-powered embedded systems.
• ML/DL algorithms supporting an embedded system’s (complex) design phases.
• ML/DL applications leveraging embedded (low-power/distributed) systems.
• Apply ML/DL models to test the data path.
• Embedded ML/DL models to add security modules to embedded systems.
• Methods and tools for parallel large-scale ML/DL applications.
• Adding ML/DL application support to edge/fog devices.
• Porting distributed deep learning into embedded systems.
• Building embedded systems to support hyperparameter optimisation.
• Incorporation of predictive models to improve the performance of scientific/industrial applications.
• Building tools and infrastructure to improve the usability of ML/DL models in scientific/industrial applications.
• Frameworks for optimising HPC ecosystems for embedding ML/DL models.
• Porting ML compilers into embedded platforms.
• Architectures for approximate computing.

Dr. Antoni Portero
Dr. Alberto Scionti
Dr. Somnath Mazumdar
Prof. Dr. Fujita Masahiro
Prof. Dr. Akash Kumar
Guest Editors

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• low power
• high performance
• machine learning
• deep learning
• embedded systems
• hardware accelerators