  Spiking neural network (SNN) is a brain-inspired model which has more
spatio-temporal information processing capacity and computational energy
efficiency. However, with the increasing depth of SNNs, the memory problem
caused by the weights of SNNs has gradually attracted attention. Inspired by
Artificial Neural Networks (ANNs) quantization technology, binarized SNN (BSNN)
is introduced to solve the memory problem. Due to the lack of suitable learning
algorithms, BSNN is usually obtained by ANN-to-SNN conversion, whose accuracy
will be limited by the trained ANNs. In this paper, we propose an ultra-low
latency adaptive local binary spiking neural network (ALBSNN) with accuracy
loss estimators, which dynamically selects the network layers to be binarized
to ensure the accuracy of the network by evaluating the error caused by the
binarized weights during the network learning process. Experimental results
show that this method can reduce storage space by more than 20 % without losing
network accuracy. At the same time, in order to accelerate the training speed
of the network, the global average pooling(GAP) layer is introduced to replace
the fully connected layers by the combination of convolution and pooling, so
that SNNs can use a small number of time steps to obtain better recognition
accuracy. In the extreme case of using only one time step, we still can achieve
92.92 %, 91.63 % ,and 63.54 % testing accuracy on three different datasets,
FashionMNIST, CIFAR-10, and CIFAR-100, respectively.
