The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
The paper introduces a spike‑aware, gradient‑based mixed‑precision quantization framework for Spiking Neural Networks (SNNs), which assigns bit‑widths adaptively using first‑order gradient information. This approach avoids the memory and computational overhead of Hessian‑based methods like HAWQ‑V2 and is evaluated on VGG, ResNet, and Vision Transformer architectures across CIFAR‑10/100, ImageNet, and DVS‑CIFAR‑10 datasets. On ResNet‑34 with ImageNet, the method attains 73.30% accuracy at 4.78× compression, matching HAWQ‑V2 while reducing memory usage by 2.31× and bit‑allocation runtime by 100×, and it demonstrates energy‑efficient deployment without performance loss.
SpikeQ introduces a spike‑guided post‑training quantization framework for spiking neural networks, selecting weight levels by simulating downstream neurons to minimize spike errors and designing a reset‑aware grid for pre‑fire membrane potential quantization that avoids spike flips. The method outperforms existing grids on seven image‑processing benchmarks, maintaining full‑precision accuracy with 2‑bit pre‑fire potentials and staying within 2.59 points of full precision at 4 bits while drastically reducing memory and energy usage.
arXiv:2409. 08290v5 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) promise higher energy efficiency over conventional Quantized Artificial Neural Networks (QNNs) due to their event-driven, spike-based computation.
arXiv:2608. 07066v1 Announce Type: new Abstract: Spiking neural networks (SNNs) enable sparse and event-driven computation, but their low-bit deployment remains incomplete because recurrent membrane states are commonly retained in floating point even after weight quantization.
Deploying deep learning models on edge CPUs is bottlenecked by computational and memory constraints. Mixed-precision quantization promises to reduce inference latency while preserving accuracy. Howeve...
The paper introduces RAMP, a method for robust adaptive mixed‑precision quantization of vision models on edge CPUs. It evaluates 13 sensitivity metrics across four neural networks, finding that Jensen‑Shannon Divergence consistently identifies layers that can be safely quantized. Using K‑Means clustering on these metrics, RAMP achieves near‑lossless accuracy with an average 1.81× speed‑up, while cautioning against excluding low‑speed‑up layers that can fragment the computational graph.