An artificial retina neuron is proposed and implemented by CMOS technology. It can be used as an image sensor in the Artificial Intelligence (AI) field with the benefit of ultra-low power consumption. The artificial neuron can generate signals in spike shape with pre-designed frequencies under different light intensities. The power consumption is reduced by removing the film capacitor. The comparator is adopted to improve the stability of the circuit, and the power consumption of the comparator is optimized. The power consumption of the proposed CMOS neuron circuit is suppressed. The ultra-low-power artificial neuron with variable threshold shows a frequency range of 0.8–80 kHz when the input current is varied from 1 pA to 150 pA. The minimum DC power is 35 pW when the input current is 5 pA. The minimum energy of the neuron is 3 fJ. The proposed ultra-low-power artificial retina neuron has wide potential applications in the field of AI.
With the rapid growth of big data information and the continuous iteration progress of CPU architecture, the implementation of a new memory-based cache architecture is urgent and challenging. In the paper, a CPU cache architecture system based on MRAM is built. Firstly, the performance of SRAM, STT-MRAM and SOT-MRAM as caches from 8 kb to 32 Mb is evaluated. Secondly, by summarizing the performance of SRAM and MRAM in different cache levels, a new quad-core CPU cache architecture design scheme with SOT-MRAM as the first level of cache and STT-MRAM as the second level of cache is determined. Thirdly, the built cache system is simulated. A non-inclusive strategy is proposed to replace the inclusive strategy in order to solve the problem of high dynamic energy of STT-MRAM at the second level. The idea of having a quad-core CPU dynamically share the second-level cache is proposed in the paper. Finally, the caching system in the paper is compared with the other previous works, showing up to 60.78% energy consumption advantage and 33.22% leakage power advantage. The proposed MRAM-based CPU cache system and the corresponding cache strategy have potential application with the benefits of low power and less area.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.