A power-adaptive neuron model and circuit implementation

Regarding the performance degradation and battery life issues faced by mobile
smart devices during low energy supply, this article thoroughly explores the
strategies employed by biological neurons to stabilize spike emission frequency
and reduce power consumption during low energy supply, as well as the characteristics
of myelin sheath in reducing power consumption. A power-adaptive
neuron (PAN) model and its corresponding power-adaptive neuron circuit system
(PANCS) are proposed, which adaptively adjust power consumption according
to energy supply conditions. Simulation and practical experiments both indicate
that PANCS has acquired power-adaptive adjustment capability (PAAC), maintaining
stable spike emission frequency when the system is under insufficient
energy supply. This ability increases with the degree of myelination of PANCS.
Power consumption analysis indicates that both PAAC and myelination lead to
a reduction in power consumption for PANCS when energy supply is insufficient.
Noise experiments demonstrate that the efficacy of PAAC entails sacrificing the
robustness of PANCS, and myelination cannot reverse the decrease in robustness.
Research findings of this paper endow neural morphology networks with the ability
to adaptively adjust power consumption according to energy supply conditions
to cope with extreme situations, providing new insights for the development of
AI.