Fetching the paper…
Reading the bibliography…
Biologically inspired Spiking Neural Networks (SNNs) have attracted significant attention for their ability to provide extremely energy-efficient machine intelligence through event-driven operation and sparse activities.
Wafer-scale integration of analog neural networks
Johannes Schemmel and et.al · 2008
Earlier work this paper cites.
Accelerated simulation of spiking neural networks using gpus
Andreas K Fidjeland and et.al · 2010
Earlier work this paper cites.
Adult neurogenesis in the mammalian brain: significant answers and significant questions
Guo-li Ming and et.al · 2011
Earlier work this paper cites.
Dynamics of hippocampal neurogenesis in adult humans
Kirsty L Spalding and et.al · 2013
Earlier work this paper cites.
Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and et.al · 2018
Earlier work this paper cites.
Loihi: A neuromorphic manycore processor with on-chip learning
Mike Davies and et.al · 2018
Earlier work this paper cites.
Stdp-based pruning of connections and weight quantization in spiking neural networks for energy-efficient recognition
Nitin Rathi and et.al · 2018
Earlier work this paper cites.
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
Decebal Constantin Mocanu and et.al · 2018
Earlier work this paper cites.
The democratization of artificial intelligence: One library’s approach
Thomas K Finley · 2019
Earlier work this paper cites.
Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks
Emre O Neftci and et.al · 2019
Cited alongside, same era.
Rigging the lottery: Making all tickets winners
Utku Evci and et.al · 2020
Cited alongside, same era.
Spikingjelly
Wei Fang and et.al · 2020
Cited alongside, same era.
Accelerating framework of transformer by hardware design and model compression co-optimization
Panjie Qi and et.al · 2021
Cited alongside, same era.
Accommodating transformer onto fpga: Coupling the balanced model compression and fpga-implementation optimization
Panjie Qi and et.al · 2021
Cited alongside, same era.
Comprehensive snn compression using admm optimization and activity regularization
Lei Deng and et.al · 2021
Deep residual learning in spiking neural networks
Wei Fang and et.al · 2021
Later among the works it cites.
Do we actually need dense over-parameterization? in-time over-parameterization in sparse training
Shiwei Liu and et.al · 2021
Later among the works it cites.
Sparse training via boosting pruning plasticity with neuroregeneration
Shiwei Liu and et.al · 2021
Later among the works it cites.
Exploring lottery ticket hypothesis in spiking neural networks
Youngeun Kim and et.al · 2022
Later among the works it cites.
Codg-reram: An algorithm-hardware co-design to accelerate semi-structured gnns on reram
Yixuan Luo and et.al · 2022
Later among the works it cites.
Sparse progressive distillation: Resolving overfitting under pretrain-and-finetune paradigm
Shaoyi Huang and et.al · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Accelerating transformer-based deep learning models on fpgas using column balanced block pruning
Hongwu Peng and et.al · 2021
Cited alongside, same era.
Et: re-thinking self-attention for transformer models on gpus
Shiyang Chen and et.al · 2021
Cited alongside, same era.
Connection pruning for deep spiking neural networks with on-chip learning
Thao NN Nguyen and et.al · 2021
Cited alongside, same era.
Later among the works it cites.
Towards sparsification of graph neural networks
Hongwu Peng and et.al · 2022
Later among the works it cites.
Dynamic sparse training via balancing the exploration-exploitation trade-off
Shaoyi Huang and et.al · 2022
Later among the works it cites.
Syncnn: Evaluating and accelerating spiking neural networks on fpgas
Sathish Panchapakesan and et.al · 2022
Later among the works it cites.