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We designed a simple neural segmentation model that is suitable for analog circuit implementation. The model consists of excitable neural oscillators and adaptive synapses, where the learning is governed by a symmetric spike-timing dependent plasticity (STDP). We numerically demonstrate basic operations of the proposed model as well as fundamental circuit operations using a simulation program with integrated circuit emphasis (SPICE).

Demands for low-energy microcontrollers have been increasing in recent years. Since most microcontrollers achieve user programmability by integrating nonvolatile (NV) memories such as flash memories for storing their programs, the large power consumption required in accessing an NV memory has become a major problem. This problem becomes critical when the power supply voltage of NV microcontrollers is decreased. We can solve this problem by introducing an instruction cache, thus reducing the access frequency of the NV memory. Unlike general-purpose microprocessors, microcontrollers used for real-time applications in embedded systems must accurately calculate program execution time prior to its execution. Therefore, we introduce a "transparent" instruction cache, which does not change the existing NV microcontroller's cycle-level execution time, for reducing power and energy consumption, but not for improving the processing speed. We have conducted detailed microar chitecture design based on the architecture of a major industrial microcontroller, and we evaluated power and energy consumption for several benchmark programs. Our evaluation shows that the proposed instruction cache can successfully reduce energy consumption in a fairly wide range of practical NV microcontroller configurations.

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