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Call for Papers

A new wave of intelligent computing, driven by recent advances in machine learning and cognitive algorithms coupled with process technology and new design methodologies, has the potential to usher unprecedented disruption in the way conventional computing solutions are designed and deployed. These new and innovative approaches often provide an attractive and efficient alternative not only in terms of performance but also power, energy, and area.

A key class of these intelligent solutions is providing real-time, on-device cognition at the edge to enable many novel applications including vision and image processing, language translation, autonomous driving, malware detection, and gesture recognition. Naturally, these applications have diverse requirements for performance, energy, reliability, accuracy, and security that demand a holistic approach to designing the hardware, software, and intelligence algorithms to achieve the best power, performance, and area (PPA).

The goal of this workshop is to provide a forum for researchers who are exploring novel ideas in the field of energy efficient machine learning and artificial intelligence for embedded applications. We also hope to provide a solid platform for forging relationships and exchange of ideas between the industry and the academic world through discussions and active collaborations.

List of Potential Ideas

• Computing techniques for IoT, Automotive, and mobile intelligence
• Exploration new and efficient applications of machine learning
• Machine learning benchmarks, workloads and their characterization
• Performance and bottleneck analysis, profiling and synthesis of workloads
• Simulation and emulation techniques, frameworks and platforms for neural networks
• Energy efficient techniques and solutions for neural networks
• Communication and computation overlapping and load balancing techniques
• Efficient hardware proposals to implement neural networks
• Power and performance efficient memory architectures
• Exploring the interplay between precision, performance, power and energy
• Approximation, quantization and reduced precision computing techniques
• Power, Performance and Area (PPA) based comparison of neural networks
• Exploration of new areas, domains and applications where machine learning can be applied efficiently
• Efficient learning techniques -- supervised vs unsupervised
• Improvements over conventional training techniques
• Inference vs Training comparison and analysis in terms of power, performance and complexity
• Hardware/software techniques to exploit sparsity and locality
• Support vector machines (SVM) based solutions
• Systolic array based architectures and systems for machine learning applications
• Security and privacy challenges and building secure systems

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