Advanced AI Hardware Designs Based on FPGAs

Dear Colleagues,

Machine learning (ML) and artificial intelligence (AI) technology have revolutionized how computers run cognitive tasks based on a massive amount of observed data. As more industries are adopting the technology, we are facing a fast-growing demand for new hardware that enables faster and more energy-efficient processing in AI workloads.

In recent years, traditional hardware vendors such as Intel and Nvidia as well as new start-up companies such as Graphcore, Wave Computing, and Habana have tried to offer the best computing platform for complex ML algorithms. Although GPU is still the preferred computing platform due to its large userbase and well-established programming interface, its top spot is not forever safe, due to its low hardware utilization and bad energy efficiency.

On top of energy efficiency and programming easiness, how to adapt fast-changing AI/ML algorithms is another hot topic in AI hardware. FPGA has a clear benefit on this point, as it can reprogram or amend its processing quickly with a relatively low power budget. In this Special Issue, we invite the latest developments in the field of advanced AI hardware design based on FPGA, which can show the device’s strengths, such as hardware/software co-design, customization, and scalability over other types of hardware.

Topics include but are not limited to:

• DNN inference/training accelerators on FPGAs;
• Multi-FPGA approaches for scalable ML acceleration;
• Distributed deep learning architecture on multiple FPGAs;
• Hardware/Software co-design for energy-efficient ML on FPGA;
• Narrow-precision and efficient floating-point representation on FPGA for ML applications;
• Design automation from the ML algorithm to FPGAs;
• Soft DNN processor to cover a wide range of ML applications.

Prof. Dr. Joo-Young Kim
Guest Editor

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