In recent years, driven by the computational demands of data-intensive applications such as artificial intelligence and scientific computing, analog computing has gained renewed interest. Given the diversity of computational tasks and recent advancements in analog CMOS circuits and resistive memory technologies, we refer to the evolving landscape as modern analog computing.
The paper introduces a fully analog readout architecture for vector-matrix multiplication in spiking neural networks that uses voltage-to-time conversion instead of conventional current-mode circuits. By sensing the column voltage directly, the design eliminates the need for current summing and scaling circuitry, thereby reducing area and power consumption. Post-layout simulations of a 10×1 SNN in 130 nm CMOS and tests on a 64×10 digit‑classification network demonstrate the feasibility and efficiency of the proposed approach.
By Elia Mateu-Barriendos, \'Alvaro G\'omez-Pau, Josep Rius, Daniel Arum\'i, Rosa Rodr\'iguez-Monta\~n\'es, Salvador Manich
arXiv:2609.36584v1 Announce Type: new
Abstract: Analog in-memory computing (AIMC) offers an alternative for model training by executing matrix operations directly where weights are stored. However, s...
By Zhaoxian Wu, Tayfun Gokmen, Omobayode Fagbohungbe, T. Patrick Xiao, Tianyi Chen
arXiv:2607. 15123v1 Announce Type: cross Abstract: Recent FPGAs have improved deep learning (DL) inference efficiency through dedicated tensor blocks and in-BRAM computation.
By Jiajun Hu, Ruthwik Reddy Sunketa, Lei Zhao, Archit Gajjar, Luca Buonanno, Aman Arora
arXiv:2606. 02781v1 Announce Type: cross Abstract: Deep neural networks (DNNs) have achieved state-of-the-art performance across diverse domains.
By Sohan Salahuddin Mugdho, Md. Shahedul Hasan, Brahmdutta Dixit, Yang Lv, Jian-Ping Wang, Cheng Wang
arXiv:2605. 11855v2 Announce Type: replace-cross Abstract: Sequence learning is dominated by Transformers and parallelizable recurrent neural networks (RNNs) such as state-space models, yet learning long-term dependencies remains challenging, and state-of-the-art designs trade power consumption for performance.
By Julien Brandoit, Arthur Fyon, Damien Ernst, Guillaume Drion