LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units
Transformer models have demonstrated high accuracy in numerous applications but have
high complexity and lack sequential processing capability making them ill-suited for many …
high complexity and lack sequential processing capability making them ill-suited for many …
Spiking Neural Networks with Dynamic Time Steps for Vision Transformers
Spiking Neural Networks (SNNs) have emerged as a popular spatio-temporal computing
paradigm for complex vision tasks. Recently proposed SNN training algorithms have …
paradigm for complex vision tasks. Recently proposed SNN training algorithms have …
Can we get the best of both Binary Neural Networks and Spiking Neural Networks for Efficient Computer Vision?
Binary Neural networks (BNN) have emerged as an attractive computing paradigm for a
wide range of low-power vision tasks. However, state-of-the-art (SOTA) BNNs do not yield …
wide range of low-power vision tasks. However, state-of-the-art (SOTA) BNNs do not yield …