2 citations · 2 across the 6 of their papers we have counts for
7 papers
AgenticSizing: A Large Language Model-based Multi-Agent Framework for Analog Circuit Sizing
Yijia Hao, Pratibha Verma, Dongxu Guo +4
Analog circuit sizing remains a challenging and time-consuming task due to the large design space, strong performance trade-offs, and increasing circuit complexity in scaled techno…
A flexible language model-assisted electronic design automation framework
Cristian Sestito, Panagiota Kontou, Pratibha Verma +5
Large language models (LLMs) are transforming electronic design automation (EDA) by enhancing design stages such as schematic design, simulation, netlist synthesis, and place-and-r…
Guided Tensor Lifting
Yixuan Li, José Wesley de Souza Magalhães, Alexander Brauckmann +2
Domain-specific languages (DSLs) for machine learning are revolutionizing the speed and efficiency of machine learning workloads as they enable users easy access to high-performanc…
What I cannot execute, I do not understand: Training and Evaluating LLMs on Program Execution Traces
Jordi Armengol-Estapé, Quentin Carbonneaux, Tianjun Zhang +8
Code generation and understanding are critical capabilities for large language models (LLMs). Thus, most LLMs are pretrained and fine-tuned on code data. However, these datasets ty…
Forklift: An Extensible Neural Lifter
Jordi Armengol-Estapé, Rodrigo C. O. Rocha, Jackson Woodruff +2
The escalating demand to migrate legacy software across different Instruction Set Architectures (ISAs) has driven the development of assembly-to-assembly translators to map between…
mlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR using Program Synthesis
Alexander Brauckmann, Elizabeth Polgreen, Tobias Grosser +1
MLIR is an emerging compiler infrastructure for modern hardware, but existing programs cannot take advantage of MLIR's high-performance compilation if they are described in lower-l…
Rewriting History: Repurposing Domain-Specific CGRAs
Jackson Woodruff, Thomas Koehler, Alexander Brauckmann +3
Coarse-grained reconfigurable arrays (CGRAs) are domain-specific devices promising both the flexibility of FPGAs and the performance of ASICs. However, with restricted domains come…