SAMpLE is an open‑source SystemC‑AMS framework that treats machine learning models as first‑class Timed Dataflow components via a standardized plug‑and‑play interface. It offers a native C++ backend for online training of lightweight models and an offline backend that runs externally developed models without re‑implementation. By using ONNX as a model exchange format, SAMpLE enables the integration and evaluation of diverse ML solutions within a single, reproducible simulation workflow.
By Andrei Mihai Albu, Sara Vinco
arXiv:2602. 05999v3 Announce Type: replace Abstract: How does the amount of compute available to a reinforcement learning (RL) policy affect its learning?
By Raj Ghugare, Micha{\l} Bortkiewicz, Alicja Ziarko, Benjamin Eysenbach
arXiv:2608. 00029v1 Announce Type: cross Abstract: The performance of deep learning models at scale relies heavily on how effectively high-level mathematical operations are mapped to underlying physical hardware.
By Adwaid Suresh, Aparna A, Harshini V M, Jona Delcy C A, Killi Uma Maheswara Rao, Ram Charan Golla, Surendra Vendra
arXiv:2505. 23131v2 Announce Type: replace Abstract: We study the problem of assigning operations in a dataflow graph to devices to minimize execution time in a work-conserving system, with emphasis on complex machine learning workloads.
By Xinyu Yao, Daniel Bourgeois, Abhinav Jain, Yuxin Tang, Jiawen Yao, Zhimin Ding, Arlei Silva, Chris Jermaine
arXiv:2603. 23878v3 Announce Type: replace-cross Abstract: The parameterized CROWN analysis, a.
By Henry LeCates, Haoze Wu
arXiv:2607. 02624v1 Announce Type: cross Abstract: Modern automotive software architectures comprise large sets of mixed-criticality functions executing on shared multi-core platforms with strict real-time and end-to-end timing requirements.
By Silviu S. Craciunas, Christian Hakert, Jian-Jia Chen, Zden\v{e}k Hanz\'alek, Paul Pop