arXiv:2604. 02429v2 Announce Type: replace-cross Abstract: Convolutional neural networks (CNNs) have transformed image processing, but the energy consumption and inference latency of electronic based implementations remain fundamental bottlenecks.
By Saurabh Ranjan, Sonika Thakral, Amit Sehgal
The paper introduces a hardware‑aware framework that uses genetic programming to evolve layer‑specific scalar functions for Vision Transformers, replacing traditional LayerNorm with efficient, heterogeneous approximations. By applying a post‑training re‑alignment strategy, the method eliminates the need for full model retraining while achieving 90‑93% variance capture and recovering over 84% of ImageNet‑1K Top‑1 accuracy for ViT‑B and ViT‑L. The resulting architecture removes the global reduction bottleneck, reducing arithmetic complexity and off‑chip memory traffic, thereby enabling efficient deployment of ViTs on edge accelerators.
By Kieran Carrigg, Sigur de Vries, Amirhossein Sadough, Marcel van Gerven
The paper introduces LCAP, a method for adapting photonic neural networks to real hardware by learning a shared correction from a population of chips and then personalizing each chip using only 32 fixed output probes. LCAP decomposes adaptation into a transferable population correction and a probe‑inferred latent personalization, allowing feed‑forward calibration without device‑specific optimization. Experiments on a simulated three‑layer 64‑mode MZI network show accuracy improvements from 80.4% to 93.4% and significant gains on unseen chips.
By Tianyu Gao, Guantian Zheng
arXiv:2607. 04422v1 Announce Type: cross Abstract: Recent NVFP4 pretraining methods mainly target transformer linear layers, leaving optimizer states, optimizer arithmetic and attention underexplored in 4-bit pipelines.
By Siyu Ding, Mingchuan Ma, Jiabo Tong, Xingrun Xing, Ziming Wang, Guoqi Li
arXiv:2607. 18540v1 Announce Type: cross Abstract: Robotic perception pipelines increasingly rely on large vision backbones deployed on SWaP-constrained edge platforms, making post-training quantization (PTQ) attractive for real-time inference.
By Hamidreza Yaghoubi Araghi, Parastoo Pilevar, Ming C. Lin
arXiv:2607. 28418v1 Announce Type: cross Abstract: Pruning is a promising approach for improving the efficiency of LLMs.
By Haozhe Hu, Hao Wu, Peiran Yin, Chao Han, Yunpu Ma, Xiaoyu Shen