  We propose Multiplier-less INTeger (MINT) quantization, a uniform
quantization scheme that efficiently compresses weights and membrane potentials
in spiking neural networks (SNNs). Unlike previous SNN quantization methods,
MINT quantizes memory-intensive membrane potentials to an extremely low
precision (2-bit), significantly reducing the memory footprint. MINT also
shares the quantization scaling factor between weights and membrane potentials,
eliminating the need for multipliers required in conventional uniform
quantization. Experimental results show that our method matches the accuracy of
full-precision models and other state-of-the-art SNN quantization techniques
while surpassing them in memory footprint reduction and hardware cost
efficiency at deployment. For example, 2-bit MINT VGG-16 achieves 90.6%
accuracy on CIFAR-10, with roughly 93.8% reduction in memory footprint from the
full-precision model and 90% reduction in computation energy compared to
vanilla uniform quantization at deployment. The code is available at
https://github.com/Intelligent-Computing-Lab-Yale/MINT-Quantization.
