arXiv AI
Syn2Logic: End-to-End Neuromorphic Design Automation
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Jun 5
Surrogate Neural Architecture Codesign Package (SNAC-Pack)
arXiv:2605. 16138v2 Announce Type: replace Abstract: Neural architecture search (NAS) is a powerful approach for automating model design, but existing methods often optimize for accuracy alone or rely on proxy metrics such as bit operations (BOPs) that correlate poorly with hardware cost.
arXiv Machine Learning
Jul 31
SNAC-Pack 2.0: Scaled-Out Surrogate Neural Architecture Codesign
arXiv:2605. 16138v3 Announce Type: replace Abstract: Neural architecture search (NAS) is a powerful approach for automating model design, but existing methods often optimize for accuracy alone or rely on proxy metrics such as bit operations (BOPs) that correlate poorly with hardware cost.
arXiv AI
Jun 10
Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA
arXiv:2606. 11117v1 Announce Type: cross Abstract: Designing FPGA-based accelerators for modern artificial intelligence workloads requires exploring a large and complex hardware design space that involves architectural parameters, data flow strategies, and memory hierarchies, making the process very time consuming.
Hugging Face Trending Papers
Aug 7
HLSmith: An Expert-Guided Agentic Framework for C/C++-to-HLS Translation
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. Even with high-level synthesis (HLS), designers still need extensive hardware expertise to build high-performance accelerators.
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.
arXiv AI
Aug 14
Automated Design Optimization via Strategic Search with Large Language Models
arXiv:2511. 22651v2 Announce Type: replace-cross Abstract: Optimization methods have long advanced many fields, yet they struggle when faced with design problems where the search space and design parameters are difficult to define.