Tootfinder

SENMAP: Multi-objective data-flow mapping and synthesis for hybrid scalable neuromorphic systems
Prithvish V Nembhani, Oliver Rhodes, Guangzhi Tang, Alexandra F Dobrita, Yingfu Xu, Kanishkan Vadivel, Kevin Shidqi, Paul Detterer Mario Konijnenburg, Gert-Jan van Schaik, Manolis Sifalakis, Zaid Al-Ars, Amirreza Yousefzadeh
https://…

SENMAP: Multi-objective data-flow mapping and synthesis for hybrid scalable neuromorphic systems
This paper introduces SENMap, a mapping and synthesis tool for scalable, energy-efficient neuromorphic computing architecture frameworks. SENECA is a flexible architectural design optimized for executing edge AI SNN/ANN inference applications efficiently. To speed up the silicon tape-out and chip design for SENECA, an accurate emulator, SENSIM, was designed. While SENSIM supports direct mapping of SNNs on neuromorphic architectures, as the SNN and ANNs grow in size, achieving optimal mapping fo…

Niets om je zorgen over te maken natuurlijk.
We plakken er gewoon weer een slaaploos nachtje aan vast. Echt uitstekend op optimaal te kunnen functioneren.

Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources
Roopkatha Banerjee, Prince Modi, Jinal Vyas, Chunduru Sri Abhijit, Tejus Chandrashekar, Harsha Varun Marisetty, Manik Gupta, Yogesh Simmhan
https://arxiv.org/abs/2507.02295

Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources
With the recent improvements in mobile and edge computing and rising concerns of data privacy, Federated Learning(FL) has rapidly gained popularity as a privacy-preserving, distributed machine learning methodology. Several FL frameworks have been built for testing novel FL strategies. However, most focus on validating the learning aspects of FL through pseudo-distributed simulation but not for deploying on real edge hardware in a distributed manner to meaningfully evaluate the federated aspects…

A scalable and programmable optical neural network in a time-synthetic dimension
Bei Wu, Yudong Ren, Rui Zhao, Haiyao Luo, Fujia Chen, Li Zhang, Hongsheng Chen, Yihao Yang
https://arxiv.org/abs/2507.02297

A scalable and programmable optical neural network in a time-synthetic dimension
Programmable optical neural networks (ONNs) can offer high-throughput and energy-efficient solutions for accelerating artificial intelligence (AI) computing. However, existing ONN architectures, typically based on cascaded unitary transformations such as Mach-Zehnder interferometer meshes, face inherent scalability limitations due to spatial encoding, which causes optical components and system complexity to scale quadratically with network size. A promising solution to this challenge is the use…

This https://arxiv.org/abs/2505.18780 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

One Policy but Many Worlds: A Scalable Unified Policy for Versatile Humanoid Locomotion
Humanoid locomotion faces a critical scalability challenge: traditional reinforcement learning (RL) methods require task-specific rewards and struggle to leverage growing datasets, even as more training terrains are introduced. We propose DreamPolicy, a unified framework that enables a single policy to master diverse terrains and generalize zero-shot to unseen scenarios by systematically integrating offline data and diffusion-driven motion synthesis. At its core, DreamPolicy introduces Humanoid…

Split-Merge Revisited: A Scalable Approach to Generalized Eigenvalue Problems
Xiaozhi Liu, Yong Xia
https://arxiv.org/abs/2507.02389 https://

Split-Merge Revisited: A Scalable Approach to Generalized Eigenvalue Problems
The generalized eigenvalue problem (GEP) serves as a cornerstone in a wide range of applications in numerical linear algebra and scientific computing. However, traditional approaches that aim to maximize the classical Rayleigh quotient often suffer from numerical instability and limited computational efficiency, especially in large-scale settings. In this work, we explore an alternative difference-based formulation of GEP by minimizing a structured quadratic polynomial objective, which enables …

Selenskyj: "Es war eine brutale, schlaflose Nacht"
Der jüngste russische Angriff auf die Ukraine in der Nacht war einer der bisher größten, teilte der ukrainische Präsident Wolodymyr Selenskyj auf X mit. Er nannte den Angriff "absichtlich massiv und zynisch". "Es war eine brutale, schlaflose Nacht", so Selenskyj. Erst gegen 9 Uhr morgens endete der Luftalarm in Kiew. Russland zeige erneut, dass es n…
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Massive russische Angriffe auf Kiew
Erneut hat es in der Nacht russische Angriffe auf die ukrainische Hauptstadt Kiew und das Umland gegeben - sie dauerten mehrere Stunden an. Es gab mehrere Verletzte und Schäden in sechs der zehn Kiewer Bezirke.

A Scalable Exponential Random Graph Model: Amortised Hierarchical Sequential Neural Posterior Estimation with Applications in Neuroscience
Yefeng Fan, Simon Richard White
https://arxiv.org/abs/2506.04558

A Scalable Exponential Random Graph Model: Amortised Hierarchical Sequential Neural Posterior Estimation with Applications in Neuroscience
Exponential Random Graph Models (ERGMs) are an inferential model for analysing statistical networks. Recent development in ERGMs uses hierarchical Bayesian setup to jointly model a group of networks, which is called a multiple-network Exponential Random Graph Model (MN-ERGMs). MN-ERGM has been successfully applied on real-world resting-state fMRI data from the Cam-CAN project to infer the brain connectivity on aging. However, conventional Bayesian ERGM estimation approach is computationally int…

Towards provable probabilistic safety for scalable embodied AI systems
Linxuan He, Qing-Shan Jia, Ang Li, Hongyan Sang, Ling Wang, Jiwen Lu, Tao Zhang, Jie Zhou, Yi Zhang, Yisen Wang, Peng Wei, Zhongyuan Wang, Henry X. Liu, Shuo Feng
https://arxiv.org/abs/2506.05171

Towards provable probabilistic safety for scalable embodied AI systems
Embodied AI systems, comprising AI models and physical plants, are increasingly prevalent across various applications. Due to the rarity of system failures, ensuring their safety in complex operating environments remains a major challenge, which severely hinders their large-scale deployment in safety-critical domains, such as autonomous vehicles, medical devices, and robotics. While achieving provable deterministic safety--verifying system safety across all possible scenarios--remains theoretic…

This https://arxiv.org/abs/2409.16967 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning
Autonomous robots are widely utilized for mapping and exploration tasks due to their cost-effectiveness. Multi-robot systems offer scalability and efficiency, especially in terms of the number of robots deployed in more complex environments. These tasks belong to the set of Multi-Robot Informative Path Planning (MRIPP) problems. In this paper, we propose a deep reinforcement learning approach for the MRIPP problem. We aim to maximize the number of discovered stationary targets in an unknown 3D …