52 citations · 130 across the 7 of their papers we have counts for
7 papers
CircuitVAE: Efficient and Scalable Latent Circuit Optimization
Jialin Song, Aidan Swope, Robert Kirby +4
Automatically designing fast and space-efficient digital circuits is challenging because circuits are discrete, must exactly implement the desired logic, and are costly to simulate…
Polaris: A Safety-focused LLM Constellation Architecture for Healthcare
Subhabrata Mukherjee, Paul Gamble, Markel Sanz Ausin +23
We develop Polaris, the first safety-focused LLM constellation for real-time patient-AI healthcare conversations. Unlike prior LLM works in healthcare focusing on tasks like questi…
GraPhSyM: Graph Physical Synthesis Model
Ahmed Agiza, Rajarshi Roy, Teodor Dumitru Ene +3
In this work, we introduce GraPhSyM, a Graph Attention Network (GATv2) model for fast and accurate estimation of post-physical synthesis circuit delay and area metrics from pre-phy…
LeanDojo: Theorem Proving with Retrieval-Augmented Language Models
Kaiyu Yang, Aidan M. Swope, Alex Gu +6
Large language models (LLMs) have shown promise in proving formal theorems using proof assistants such as Lean. However, existing methods are difficult to reproduce or build on, du…
PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning
Rajarshi Roy, Jonathan Raiman, Neel Kant +6
In this work, we present a reinforcement learning (RL) based approach to designing parallel prefix circuits such as adders or priority encoders that are fundamental to high-perform…
Guiding Global Placement With Reinforcement Learning
Robert Kirby, Kolby Nottingham, Rajarshi Roy +2
Recent advances in GPU accelerated global and detail placement have reduced the time to solution by an order of magnitude. This advancement allows us to leverage data driven optimi…
Can -Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?
Vitaly Kurin, Saad Godil, Shimon Whiteson +1
We present Graph--SAT, a branching heuristic for a Boolean SAT solver trained with value-based reinforcement learning (RL) using Graph Neural Networks for function approximation…