Neuromorphic circuits aim at emulating biological spiking neurons in silicon hardware. Neurons can be implemented either as analog or digital components. While the respective advantages of each approach are well known, i.e., digital designs are more simple but analog neurons are more energy efficient, there exists no clear and precise quantitative comparison of both designs. In this paper, we compare the digital and analog implementations of the same Leaky Integrate-and-Fire neuron model at the same technology node (CMOS 65 nm) with the same level of performance (SNR and maximum spiking rate), in terms of area and energy. We show that the analog implementation requires 5 times less area, and consumes 20 times less energy than the digital design. As a result, the analog neuron, in spite of its greater design complexity, is a serious contender for future large-scale silicon neural systems.

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