  Due to the fundamental limit to reducing power consumption of running deep
learning models on von-Neumann architecture, research on neuromorphic computing
systems based on low-power spiking neural networks using analog neurons is in
the spotlight. In order to integrate a large number of neurons, neurons need to
be designed to occupy a small area, but as technology scales down, analog
neurons are difficult to scale, and they suffer from reduced voltage
headroom/dynamic range and circuit nonlinearities. In light of this, this paper
first models the nonlinear behavior of existing current-mirror-based
voltage-domain neurons designed in a 28nm process, and show SNN inference
accuracy can be severely degraded by the effect of neuron's nonlinearity. Then,
to mitigate this problem, we propose a novel neuron, which processes incoming
spikes in the time domain and greatly improves the linearity, thereby improving
the inference accuracy compared to the existing voltage-domain neuron. Tested
on the MNIST dataset, the inference error rate of the proposed neuron differs
by less than 0.1% from that of the ideal neuron.
