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Cheolhyeon Kwon and Donghyun Kang
Recently, the technologies of on-device AI have been accelerated with the development of new hardware and software platforms. Therefore, many researchers and engineers focus on how to enable ML technologies on mobile devices with limited hardware resourc...
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Wenxin Yang, Xiaoli Zhi and Weiqin Tong
Current edge devices for neural networks such as FPGA, CPLD, and ASIC can support low bit-width computing to improve the execution latency and energy efficiency, but traditional linear quantization can only maintain the inference accuracy of neural netwo...
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Nikolaos Schizas, Aristeidis Karras, Christos Karras and Spyros Sioutas
The rapid emergence of low-power embedded devices and modern machine learning (ML) algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks such as TinyML have created new opportunities for ML algorithms running within ed...
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