The existing artificial intelligent (AI) computing platform represented by x86+GPU

limited by power consumption

dimension

bandwidth

environmental adaptability

and other factors

cannot be well adapted to the things and edge intelligent computing scenarios. We proposed an embedded AI computing system based on ARM (Advanced RISC Machine) + DLP (Deep Learning Processor) + SRIO (Serial RapidIO)

and elaborated the design methods and technical advantages. In study

three aspects of the system were dissertated: AI computing performance

power efficiency

and IO bandwidth

and the function and performance of the system were verified by experiments. The results show that the peak performance of the embedded AI computing system based on ARM+DLP+SRIO is up to 114.9TOPS

the energy efficiency is up to 1.03TFLOPS/W

and the IO bandwidth is up to 20Gbps. In the field of AI computing systems

its energy efficiency is better than other similar boards or systems in China

and its embedded environmental adaptability is better than that of traditional desktops and servers

so it can provide a general hardware acceleration platform for AI computing tasks in things and edge computing scenarios.