IS-SNN removes activation normalization from deep SNNs via topology-aware weight standardization folded into static weights, matching dynamic BN accuracy on ImageNet (68.05%) while cutting FPGA neuron LUT usage by 96.4%.
Micro-batch training with batch-channel normalization and weight standardization
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 5verdicts
UNVERDICTED 5roles
background 1polarities
background 1representative citing papers
SpikON introduces learnable threshold and weight techniques plus a dual-parallel SNN accelerator that cut training latency by 32% and energy by 35% while delivering 7-27x throughput gains over Apple M4 GPU and TPU-like designs.
MD Decoupling factorizes weights into fixed-norm directions and learnable per-row/column magnitudes updated at independent rates, improving Adam and Muon training stability and scale transfer without weight decay or warmup.
Manifold constraints via the new MACRO optimizer independently bound activation scales and enforce rotational equilibrium in LLM pre-training, subsuming RMS normalization and decoupled weight decay while delivering competitive performance with convergence guarantees.
A polynomial preconditioning layer controls singular value spectra of transformer weights to stabilize pre-training, shown effective on Llama-1B and supported by convergence theory for deep linear networks.
citing papers explorer
-
Intrinsically Stable Spiking Neural Networks: Overcoming the Performance Barrier in the Absence of Batch Normalization
IS-SNN removes activation normalization from deep SNNs via topology-aware weight standardization folded into static weights, matching dynamic BN accuracy on ImageNet (68.05%) while cutting FPGA neuron LUT usage by 96.4%.
-
SpikON: A Dual-Parallel and Efficient Accelerator for Online Spiking Neural Networks Learning
SpikON introduces learnable threshold and weight techniques plus a dual-parallel SNN accelerator that cut training latency by 32% and energy by 35% while delivering 7-27x throughput gains over Apple M4 GPU and TPU-like designs.
-
Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors
MD Decoupling factorizes weights into fixed-norm directions and learnable per-row/column magnitudes updated at independent rates, improving Adam and Muon training stability and scale transfer without weight decay or warmup.
-
Demystifying Manifold Constraints in LLM Pre-training
Manifold constraints via the new MACRO optimizer independently bound activation scales and enforce rotational equilibrium in LLM pre-training, subsuming RMS normalization and decoupled weight decay while delivering competitive performance with convergence guarantees.
-
PC Layer: Polynomial Weight Preconditioning for Improving LLM Pre-Training
A polynomial preconditioning layer controls singular value spectra of transformer weights to stabilize pre-training, shown effective on Llama-1B and supported by convergence theory for deep linear networks.