An Ultra Low-Power Memristive Neuromorphic Circuit for Internet of Things Smart Sensors

In this paper, we propose an ultra low-power analog neuromorphic circuit to be trained to process sensory data in the Internet of Things smart sensors where low-power and area-efficient computing is required. To reduce the operating voltage of the circuit while maintaining the performance, we focus on designing a memristive neuromorphic circuit without employing operational amplifiers. Therefore, we use the CMOS inverters as the neurons in our memristive neuromorphic circuit. We also propose ultra low-power mixed-signal input/output interfaces to make the circuit connectable to other digital components such as embedded processor. To assess the efficacy of the proposed circuit and its interfaces which include memristive neural network based A/D and D/A converters, HSPICE simulations are utilized. The results indicate that at the operating voltage of +/- 0.25 V, at least 108x (278x) reduction in the power consumption of the output (input) interface compared to that of the conventional structures is achieved. Additionally, the effectiveness of the neuromorphic circuit enhanced by the proposed interfaces is evaluated under some applications such as image recognition, human behavior analysis, and air quality predictions. The results of the study reveal that the designed neuromorphic circuits, along with the proposed A/D and D/A converters, provide an average power saving (speedup) of 2960x (37x) over the ASIC implementation in a 90-nm CMOS technology.