Tootfinder

Guaranteed Guess: A Language Modeling Approach for CISC-to-RISC Transpilation with Testing Guarantees
Ahmed Heakl, Sarim Hashmi, Chaimaa Abi, Celine Lee, Abdulrahman Mahmoud
https://arxiv.org/abs/2506.14606

Guaranteed Guess: A Language Modeling Approach for CISC-to-RISC Transpilation with Testing Guarantees
The hardware ecosystem is rapidly evolving, with increasing interest in translating low-level programs across different instruction set architectures (ISAs) in a quick, flexible, and correct way to enhance the portability and longevity of existing code. A particularly challenging class of this transpilation problem is translating between complex- (CISC) and reduced- (RISC) hardware architectures, due to fundamental differences in instruction complexity, memory models, and execution paradigms. I…

The ALMA Survey of Gas Evolution of PROtoplanetary Disks (AGE-PRO): VII. Testing accretion mechanisms from disk population synthesis
Beno\^it Tabone, Giovanni P. Rosotti, Leon Trapman, Paola Pinilla, Ilaria Pascucci, Alice Somigliana, Richard Alexander, Miguel Vioque, Rossella Anania, Aleksandra Kuznetsova, Ke Zhang, Laura M. P\'erez, Lucas A. Cieza, John Carpenter, Dingshan Deng, Carolina Agurto-Gangas, Dary A. Ru\'iz-Rodr\'iguez, Anibal Sierra, Nicol\'as T. Kurtovic, …

The ALMA Survey of Gas Evolution of PROtoplanetary Disks (AGE-PRO): VII. Testing accretion mechanisms from disk population synthesis
The architecture of planetary systems depends on the evolution of the disks in which they form. In this work, we develop a population synthesis approach to interpret the AGE-PRO measurements of disk gas mass and size considering two scenarios: turbulence-driven evolution with photoevaporative winds and MHD disk-wind-driven evolution. A systematic method is proposed to constrain the distribution of disk parameters from the disk fractions, accretion rates, disk gas masses, and CO gas sizes. We fi…

This https://arxiv.org/abs/2506.02052 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Protap: A Benchmark for Protein Modeling on Realistic Downstream Applications
Recently, extensive deep learning architectures and pretraining strategies have been explored to support downstream protein applications. Additionally, domain-specific models incorporating biological knowledge have been developed to enhance performance in specialized tasks. In this work, we introduce $\textbf{Protap}$, a comprehensive benchmark that systematically compares backbone architectures, pretraining strategies, and domain-specific models across diverse and realistic downstream protein …

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…

Peering through the veil: Investigating protoplanetary disk outer edges using backside visibility
Joel George, Carsten Dominik, Christian Ginski
https://arxiv.org/abs/2506.03624

Peering through the veil: Investigating protoplanetary disk outer edges using backside visibility
Protoplanetary disks observed in scattered light reveal essential insights into the disk's three-dimensional architecture and dust properties. These disks, which play a crucial role in planet formation, have complex structures where the visibility of the disk's backside can vary significantly based on several parameters. This study aims to explore the factors impacting backside visibility in protoplanetary disks, particularly under variations in inclination, dust distribution, grain characteris…

Protap: A Benchmark for Protein Modeling on Realistic Downstream Applications
Shuo Yan, Yuliang Yan, Bin Ma, Chenao Li, Haochun Tang, Jiahua Lu, Minhua Lin, Yuyuan Feng, Hui Xiong, Enyan Dai
https://arxiv.org/abs/2506.02052

Protap: A Benchmark for Protein Modeling on Realistic Downstream Applications
Recently, extensive deep learning architectures and pretraining strategies have been explored to support downstream protein applications. Additionally, domain-specific models incorporating biological knowledge have been developed to enhance performance in specialized tasks. In this work, we introduce $\textbf{Protap}$, a comprehensive benchmark that systematically compares backbone architectures, pretraining strategies, and domain-specific models across diverse and realistic downstream protein …

Energy-Efficient QoS-Aware Scheduling for S-NUCA Many-Cores
Sudam M. Wasala, Jurre Wolff, Yixian Shen, Anuj Pathania, Clemens Grelck, Andy D. Pimentel
https://arxiv.org/abs/2505.23351

Energy-Efficient QoS-Aware Scheduling for S-NUCA Many-Cores
Optimizing performance and energy efficiency in many-core processors, especially within Non-Uniform Cache Access (NUCA) architectures, remains a critical challenge. The performance heterogeneity inherent in S-NUCA systems complicates task scheduling due to varying cache access latencies across cores. This paper introduces a novel QoS management policy to maintain application execution within predefined Quality of Service (QoS) targets, measured using the Application Heartbeats framework. QoS me…

Prompt-Unseen-Emotion: Zero-shot Expressive Speech Synthesis with Prompt-LLM Contextual Knowledge for Mixed Emotions
Xiaoxue Gao, Huayun Zhang, Nancy F. Chen
https://arxiv.org/abs/2506.02742

Prompt-Unseen-Emotion: Zero-shot Expressive Speech Synthesis with Prompt-LLM Contextual Knowledge for Mixed Emotions
Existing expressive text-to-speech (TTS) systems primarily model a limited set of categorical emotions, whereas human conversations extend far beyond these predefined emotions, making it essential to explore more diverse emotional speech generation for more natural interactions. To bridge this gap, this paper proposes a novel prompt-unseen-emotion (PUE) approach to generate unseen emotional speech via emotion-guided prompt learning. PUE is trained utilizing an LLM-TTS architecture to ensure emo…

This https://arxiv.org/abs/2505.16968 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark
We introduce \texttt{CASS}, the first large-scale dataset and model suite for cross-architecture GPU code transpilation, targeting both source-level (CUDA~$\leftrightarrow$~HIP) and assembly-level (Nvidia SASS~$\leftrightarrow$~AMD RDNA3) translation. The dataset comprises 70k verified code pairs across host and device, addressing a critical gap in low-level GPU code portability. Leveraging this resource, we train the \texttt{CASS} family of domain-specific language models, achieving 95\% sourc…

This https://arxiv.org/abs/2505.16968 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark
We introduce \texttt{CASS}, the first large-scale dataset and model suite for cross-architecture GPU code transpilation, targeting both source-level (CUDA~$\leftrightarrow$~HIP) and assembly-level (Nvidia SASS~$\leftrightarrow$~AMD RDNA3) translation. The dataset comprises 70k verified code pairs across host and device, addressing a critical gap in low-level GPU code portability. Leveraging this resource, we train the \texttt{CASS} family of domain-specific language models, achieving 95\% sourc…