Hugging Face Trending Papers

Silinx: An Open, Browser-Based HDL Design Platform for Teaching Digital Systems on FPGA Boards

Silinx is a free, browser‑based HDL design platform that preserves the familiar Xilinx ISE Project Navigator workflow while eliminating the need to install legacy tools. It supports VHDL, Verilog, C/C++ via HLS, schematic and diagram inputs, and generates HDL automatically. Designs are simulated, built, and programmed onto real Spartan‑3E and Spartan‑6 boards over WebUSB, with an open flow that matches ISE’s bitstreams and even outperforms ISE in resource usage and timing for many designs.

arXiv AI
Aug 10

HLSmith: An Expert-Guided Agentic Framework for C/C++-to-HLS Translation

arXiv:2608. 06791v1 Announce Type: cross Abstract: Application-specific FPGA accelerators offer substantial performance and energy-efficiency gains across many application domains, but developing them is costly, often requiring months of specialized effort.

By Yuebo Luo, Ahmad Sedigh Baroughi, Philip Stachura, Le Chen, Venkatram Vishwanath, Zhenman Fang, Caiwen Ding
arXiv AI
Oct 16

FAOPs: Normal Forms and an Algebra for AI Agent Workflows

FAOPs introduces a formal framework for AI agent workflows, called Functional Agent Operating Procedures, that applies database‑grade discipline to multi‑step agent skills. It normalizes workflows into four progressive normal forms—Decomposed, Typed, Factored, and Scoped—ensuring each atomic step is type‑checked, scoped, bounded, and auditable. The framework includes a provably sound algebra for refactoring and parallelization, extensions for verified termination, optimization, and cost bounds, and an architectural classifier to assess AI necessity, all implemented in the SkillBase system with a runtime executor.

By Ali Mohammadi Shanghooshabad (JPMorgan Chase,Co.)
arXiv AI
Aug 20

Large Language Model for Verilog Code Generation: Literature Review and the Road Ahead

The article reviews the growing use of Large Language Models (LLMs) for generating Verilog code, a key hardware description language in electronic design automation. It surveys 102 papers, covering conferences, journals, and preprints, and addresses four research questions about LLM selection, datasets, techniques, and alignment strategies. The review identifies current limitations and proposes a roadmap for future research in LLM-assisted hardware design.

By Guang Yang, Wei Zheng, Xiang Chen, Dong Liang, Peng Hu, Yukui Yang, Shaohang Peng, Zhenghan Li, Jiahui Feng, Xiao Wei, Kexin Sun, Deyuan Ma, Haotian Cheng, Yiheng Shen, Xing Hu, Terry Yue Zhuo, David Lo
arXiv Machine Learning
Jul 28

Benchmarking LLMs for Verilog Design Flows

arXiv:2607. 22759v1 Announce Type: cross Abstract: Large language models (LLMs) show promise in code generation, but their capabilities to produce correct, synthesizable hardware description language (HDL) code still remain to be properly benchmarked.

By Angshuman Chakravertty, Rahul Koshti, Buddhi Prakash Sharma, Vinay Chamola
arXiv AI
Jun 3

Agent libOS: A Library-OS-Inspired Runtime for Long-Running, Capability-Controlled LLM Agents

arXiv:2606. 03895v1 Announce Type: cross Abstract: Large language model (LLM) agents are evolving from request-response assistants into long-running software actors: they maintain state across model calls, fork subtasks, wait for external events, request human authority, generate tools, and perform side effects that must be resumed and audited.

By Yingqi Zhang
Hugging Face Trending Papers
Jun 2

Agent libOS: A Library-OS-Inspired Runtime for Long-Running, Capability-Controlled LLM Agents

Large language model (LLM) agents are evolving from request-response assistants into long-running software actors: they maintain state across model calls, fork subtasks, wait for external events, request human authority, generate tools, and perform side effects that must be resumed and audited. This paper presents Agent libOS, a library-OS-inspired runtime substrate for LLM agents.

arXiv Machine Learning
Sep 21

Programming AMD XDNA NPUs with Open-source Compiler Tools: A FlashAttention Case Study

The paper reports on programming AMD XDNA NPUs for the FlashAttention workload using open‑source IRON and MLIR‑AIR compiler tools. It compares four reference designs on XDNA 1 and XDNA 2, showing that a fused kernel that keeps QKᵀ scores in local memory achieves 3.62 TFLOP/s on XDNA 2, doubling throughput and greatly improving energy efficiency over the IRON design and the integrated GPU. Roofline analysis guides when to fuse or stream operators based on each device’s ridge points, and the authors release the reference designs as open source.

By Erwei Wang, Ephrem Wu, Victor J. B. Jung, Jiajie Li, Andre Rosti, Joseph Melber, Samuel Bayliss