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## Inverter-based memristive neuromorphic circuit for ultra-low-power IoT smart applications

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Nowadays, the analysis of massive amounts of data is generally performed by remotely accessing large cloud computing resources. The cloud computing is however hindered by the security limitation, bandwidth bottleneck, and high cost. In addition, while unstructured an multimedia data (video, audio, etc.) are straightforwardly recognized and processed by the human brain, conventional digital computing architecture has major difficulties in processing this type of data, especially in real time. Another major concern for data processing, especially in the case of Internet of Things (IoT) devices which do distributed sensing and typically rely on energy scavenging, is power consumption. One of the ways to deal with the cloud computing bottlenecks is to use low -power neuromorphic circuits, which are a type of embedded intelligent circuits aimed at real-time screening and preprocessing of data before submitting the data to the cloud for further processing. This chapter explores ultra -low -power analog neuromorphic circuits for processing sensor data in the IoT devices where low -power, yet area -efficient computations are required. To reduce power consumption without losing performance, we resort to a memristive neuromorphic circuit that employs inverters instead of power-hungry op -amps. We also discuss ultra -low -power mixed -signal analog -to -digital converters (ADC) and digital -to -analog converters (DAC) to make the analog neuromorphic circuit connectable to other digital components such as an embedded processor. To illustrate how inverter -based memristive neuromorphic circuits can be exploited for reducing power and area, several case studies are presented.

Chapter Contents:

• 11.1 Introduction
• 11.2 Literature review
• 11.3 Inverter-based memristive neuromorphic circuit
• 11.3.1 Neuron circuit with memristive synapse
• 11.3.2 Input interface (DAC)
• 11.3.3 Training scheme
• 11.3.4 Output interface (ADC)
• 11.4 Results and discussion
• 11.4.1 Input interface (DAC)
• 11.4.2 Output interface (ADC)
• 11.4.3 Inverter-based memristive neuromorphic circuit
• 11.4.4 Impact of unideal condition
• 11.4.4.1 Process variation
• 11.4.4.2 Input noise
• 11.4.4.3 Limited write precision
• 11.4.4.4 Combining unideal conditions
• 11.5 Conclusion
• References

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