Artificial intelligence (AI) continues to rapidly evolve and impact more industries on a global scale. From autonomous cars to virtual personal assistants, AI has increasingly integrated into our daily lives. In particular, the use of AI at the edge is becoming widespread, as it enables real-time processing of data near the sensor rather than relying on centralized data centers. Such edge execution of AI allows for a reduction in latency, connectivity dependency, energy consumption, and cost.
Indeed, several hardware options are available for processing data in real-time, such as the central processing unit (CPU), graphical processing unit (GPU), field-programmable gate array (FPGA), system-on-chip (SoC) and application-specific integrated circuit (ASIC). Each of these technologies has its own advantages and disadvantages, and the choice depends on the specific requirements of the target application.