Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Retrieval-Guided Reinforcement Learning for Boolean Circuit Minimization
Authors: Animesh Basak Chowdhury, Marco Romanelli, Benjamin Tan, Ramesh Karri, Siddharth Garg
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This study conducts a thorough examination of learning and search techniques for logic synthesis...Our findings showcase substantial enhancements...Furthermore, ABC-RL achieves an impressive up to 9x reduction in runtime...3 EMPIRICAL EVALUATION |
| Researcher Affiliation | Academia | Animesh Basak Chowdhury1 Marco Romanelli1 Benjamin Tan2 Ramesh Karri1 Siddharth Garg1 1 New York University 2 University of Calgary |
| Pseudocode | Yes | Algorithm 1 ABC-RL: Policy agent pre-training |
| Open Source Code | No | The paper provides a reproducibility statement but does not include any explicit statement about releasing their own code or a link to a code repository. |
| Open Datasets | Yes | We consider three datasets used by logic synthesis community: MCNC Yang (1991), EPFL arithmetic and EPFL random control benchmarks Amarú et al. (2015). |
| Dataset Splits | Yes | We randomly split the 56 total netlists obtained from all three benchmarks into 23 netlists for training 13 for validation (11 MCNC, 1 EPFL-arith, 1 EPFLrand) and remaining 20 for test (see Table 1). |
| Hardware Specification | Yes | We performed the training on a server machine with one NVIDIA RTX A4000 with 16GB VRAM. |
| Software Dependencies | No | The paper describes various methods and models (e.g., GCN, BERT, Adam), citing their original papers, but it does not specify the version numbers for software libraries or dependencies used in the implementation. |
| Experiment Setup | Yes | Agents are trained for 50 epochs using Adam with an initial learning rate of 0.01. In each training epoch, we perform MCTS on all netlists with an MCTS search budget K = 512 per synthesis level...We set T = 100 and δth = 0.007 based on our validation data. |