arXiv AI June 16, 2026 By Xuqi Zhu, Huaizhi Zhang, JunKyu Lee, Jiacheng Zhu, Chandrajit Pal, Sangeet Saha, Klaus D. McDonald-Maier, Xiaojun Zhai

Mitigating scalability challenges in LUT-based neural networks via pruning optimisations

Read the original on arXiv AI →

arXiv:2407. 02362v3 Announce Type: replace-cross Abstract: Modern deep neural networks heavily rely on a large number of multiply-accumulate operations, which constitute the predominant computational cost.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

efficiency

arXiv Machine Learning

Jun 18

KANEL\'E: Kolmogorov-Arnold Networks for Efficient LUT-based Evaluation

arXiv:2512. 12850v3 Announce Type: replace-cross Abstract: Low-latency, resource-efficient neural network inference on FPGAs is essential for applications demanding real-time capability and low power.

By Duc Hoang, Aarush Gupta, Philip Harris

efficiency benchmarks

arXiv Machine Learning

Jun 26

DMuon: Efficient Distributed Muon Training with Near-Adam Overhead

arXiv:2606. 27153v1 Announce Type: cross Abstract: Matrix-orthogonalization-based optimizers, exemplified by Muon, have demonstrated strong convergence behavior across a wide range of modern deep learning workloads.

By Vincent Chen, Starrick Liu, Regis Cheng, Dance Yang, Shalfun Li, Ryan Yu, Lucy Liang, Hang Su, Roy Gan, Hao Wang, Qian Wang

llms robotics

arXiv Machine Learning

Jun 26

Hierarchical Muon: Tiled Newton-Schulz Updates for Efficient Muon Optimization

arXiv:2606. 27216v1 Announce Type: cross Abstract: Muon-type optimizers construct update directions for dense neural-network weights by applying a finite Newton-Schulz map to momentum-gradient matrices.

By Ziyuan Tang, Tianshi Xu, Yousef Saad, Yuanzhe Xi

llms rag

arXiv Machine Learning

Jul 8

BitLogic: Training Framework for Gradient-Based FPGA-Native Neural Networks

arXiv:2602. 07400v2 Announce Type: replace Abstract: Gradient-based LUT- and logic-gate-based neural networks (LUTNet, LogicNets, DiffLogic, PolyLUT, NeuraLUT, WARP-LUT, DWN, LILogicNet, LightLUT) replace multiply-accumulate arithmetic with Boolean lookups.

By Simon B\"uhrer, Andreas Plesner, Aczel Till, Roger Wattenhofer

efficiency

arXiv AI

Jul 13

Fully Trainable Deep Differentiable Logic Gate Networks and Lookup Table Networks

arXiv:2607. 09399v1 Announce Type: cross Abstract: We introduce a novel method for both partial and full optimization of the connections in deep differentiable logic gate networks (LGNs) and lookup table networks (LUTNs).

By Wout Mommen, Lars Keuninckx, Matthias Hartmann, Werner Van Leekwijck, Piet Wambacq

benchmarks

arXiv AI

Jun 24

Bitwise Systolic Array Architecture for Runtime-Reconfigurable Multi-precision Quantized Multiplication on Hardware Accelerators

arXiv:2602. 23334v2 Announce Type: replace-cross Abstract: Neural network accelerators have been widely applied to edge devices for complex tasks like object tracking, image recognition, etc.

By Yuhao Liu, Salim Ullah, Akash Kumar

computer-vision efficiency

Related stories

KANEL\'E: Kolmogorov-Arnold Networks for Efficient LUT-based Evaluation

DMuon: Efficient Distributed Muon Training with Near-Adam Overhead

Hierarchical Muon: Tiled Newton-Schulz Updates for Efficient Muon Optimization

BitLogic: Training Framework for Gradient-Based FPGA-Native Neural Networks

Fully Trainable Deep Differentiable Logic Gate Networks and Lookup Table Networks

Bitwise Systolic Array Architecture for Runtime-Reconfigurable Multi-precision Quantized Multiplication on Hardware Accelerators