7 citations · 8 across the 10 of their papers we have counts for
12 papers
VHDL-REPOBENCH: A Repository-Level Benchmark for Evaluating Large Language Models on VHDL Design Generation
Prashanth Vijayaraghavan, Akul Malhotra, Ashutosh Jadhav +2
Large Language Models (LLMs) are increasingly applied in hardware design automation, demonstrating strong potential in generating and understanding hardware description languages.…
ROSUM-MCTS: Monte Carlo Tree Search-Inspired HDL Code Summarization with Structural Rewards
Prashanth Vijayaraghavan, Charles Mackin, Luyao Shi +6
Large language models (LLMs) have shown promise in code summarization, yet their effectiveness for Hardware Description Languages (HDLs) like VHDL and Verilog remains underexplored…
StepPRM-RTL: Stepwise Process-Reward Guided LLM Fine-Tuning for Enhanced RTL Synthesis
Prashanth Vijayaraghavan, Apoorva Nitsure, Luyao Shi +2
Automatic generation of RTL code for digital hardware designs remains challenging due to long-horizon reasoning, multi-step dependencies, and strict correctness constraints in Veri…
SYMDIREC: A Neuro-Symbolic Divide-Retrieve-Conquer Framework for Enhanced RTL Synthesis and Summarization
Prashanth Vijayaraghavan, Apoorva Nitsure, Luyao Shi +5
Register-Transfer Level (RTL) synthesis and summarization are central to hardware design automation but remain challenging for Large Language Models (LLMs) due to rigid HDL syntax,…
CODMAS: A Dialectic Multi-Agent Collaborative Framework for Structured RTL Optimization
Che-Ming Chang, Prashanth Vijayaraghavan, Ashutosh Jadhav +4
Optimizing Register Transfer Level (RTL) code is a critical step in Electronic Design Automation (EDA) for improving power, performance, and area (PPA). We present CODMAS (Collabor…
Chain-of-Descriptions: Improving Code LLMs for VHDL Code Generation and Summarization
Prashanth Vijayaraghavan, Apoorva Nitsure, Charles Mackin +9
Large Language Models (LLMs) have become widely used across diverse NLP tasks and domains, demonstrating their adaptability and effectiveness. In the realm of Electronic Design Aut…
DECASTE: Unveiling Caste Stereotypes in Large Language Models through Multi-Dimensional Bias Analysis
Prashanth Vijayaraghavan, Soroush Vosoughi, Lamogha Chiazor +4
Recent advancements in large language models (LLMs) have revolutionized natural language processing (NLP) and expanded their applications across diverse domains. However, despite t…
AUTOCIRCUIT-RL: Reinforcement Learning-Driven LLM for Automated Circuit Topology Generation
Prashanth Vijayaraghavan, Luyao Shi, Ehsan Degan +2
Analog circuit topology synthesis is integral to Electronic Design Automation (EDA), enabling the automated creation of circuit structures tailored to specific design requirements.…
Self-Regulated Data-Free Knowledge Amalgamation for Text Classification
Prashanth Vijayaraghavan, Hongzhi Wang, Luyao Shi +3
Recently, there has been a growing availability of pre-trained text models on various model repositories. These models greatly reduce the cost of training new models from scratch a…
CIRCUITSYNTH: Leveraging Large Language Models for Circuit Topology Synthesis
Prashanth Vijayaraghavan, Luyao Shi, Ehsan Degan +1
Circuit topology generation plays a crucial role in the design of electronic circuits, influencing the fundamental functionality of the circuit. In this paper, we introduce CIRCUIT…
VHDL-Eval: A Framework for Evaluating Large Language Models in VHDL Code Generation
Prashanth Vijayaraghavan, Luyao Shi, Stefano Ambrogio +4
With the unprecedented advancements in Large Language Models (LLMs), their application domains have expanded to include code generation tasks across various programming languages.…
PROMINET: Prototype-based Multi-View Network for Interpretable Email Response Prediction
Yuqing Wang, Prashanth Vijayaraghavan, Ehsan Degan
Email is a widely used tool for business communication, and email marketing has emerged as a cost-effective strategy for enterprises. While previous studies have examined factors a…