  A prominent technique for reducing the memory footprint of Spiking Neural
Networks (SNNs) without decreasing the accuracy significantly is quantization.
However, the state-of-the-art only focus on employing the weight quantization
directly from a specific quantization scheme, i.e., either the post-training
quantization (PTQ) or the in-training quantization (ITQ), and do not consider
(1) quantizing other SNN parameters (e.g., neuron membrane potential), (2)
exploring different combinations of quantization approaches (i.e., quantization
schemes, precision levels, and rounding schemes), and (3) selecting the SNN
model with a good memory-accuracy trade-off at the end. Therefore, the memory
saving offered by these state-of-the-art to meet the targeted accuracy is
limited, thereby hindering processing SNNs on the resource-constrained systems
(e.g., the IoT-Edge devices). Towards this, we propose Q-SpiNN, a novel
quantization framework for memory-efficient SNNs. The key mechanisms of the
Q-SpiNN are: (1) employing quantization for different SNN parameters based on
their significance to the accuracy, (2) exploring different combinations of
quantization schemes, precision levels, and rounding schemes to find efficient
SNN model candidates, and (3) developing an algorithm that quantifies the
benefit of the memory-accuracy trade-off obtained by the candidates, and
selects the Pareto-optimal one. The experimental results show that, for the
unsupervised network, the Q-SpiNN reduces the memory footprint by ca. 4x, while
maintaining the accuracy within 1% from the baseline on the MNIST dataset. For
the supervised network, the Q-SpiNN reduces the memory by ca. 2x, while keeping
the accuracy within 2% from the baseline on the DVS-Gesture dataset.

Neural and Evolutionary Computing;Hardware Architecture;Machine Learning