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cs.CV2023
GenQ: Quantization in Low Data Regimes with Generative Synthetic Data
Yuhang Li, Youngeun Kim, Donghyun Lee +2
In the realm of deep neural network deployment, low-bit quantization presents a promising avenue for enhancing computational efficiency. However, it often hinges on the availabilit…
eess.SP2023
Rethinking Skip Connections in Spiking Neural Networks with Time-To-First-Spike Coding
Youngeun Kim, Adar Kahana, Ruokai Yin +4
Time-To-First-Spike (TTFS) coding in Spiking Neural Networks (SNNs) offers significant advantages in terms of energy efficiency, closely mimicking the behavior of biological neuron…
cs.NE2023
MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural Networks
Ruokai Yin, Yuhang Li, Abhishek Moitra +1
We propose Multiplier-less INTeger (MINT) quantization, a uniform quantization scheme that efficiently compresses weights and membrane potentials in spiking neural networks (SNNs).…