1 citations · 1 across the 7 of their papers we have counts for
8 papers
R2V Agent: Teaching SLMs When to Ask for Help
Raghu Vamshi Hemadri, Humaira Firdowse Mohammed, Rishabh Maheshwary +5
Efficient agentic systems should incur expensive frontier-model costs only on decisions where a cheaper local model is likely to fail. Existing LLM cascades usually route whole que…
TrojanLoC: LLM-based Framework for RTL Trojan Localization
Weihua Xiao, Zeng Wang, Minghao Shao +6
Hardware Trojans (HT s) are a persistent threat to integrated circuits, especially when inserted at the register-transfer level (RTL). Existing methods typically first convert the…
VeriDispatcher: Multi-Model Dispatching through Pre-Inference Difficulty Prediction for RTL Generation Optimization
Zeng Wang, Weihua Xiao, Minghao Shao +4
Large Language Models (LLMs) show strong performance in RTL generation, but different models excel on different tasks because of architecture and training differences. Prior work m…
OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction
Raghu Vamshi Hemadri, Geetha Krishna Guruju, Kristi Topollai +1
Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinical data. While large language mod…
VeriLoC: Line-of-Code Level Prediction of Hardware Design Quality from Verilog Code
Raghu Vamshi Hemadri, Jitendra Bhandari, Andre Nakkab +5
Modern chip design is complex, and there is a crucial need for early-stage prediction of key design-quality metrics like timing and routing congestion directly from Verilog code (a…
PrefixLLM: LLM-aided Prefix Circuit Design
Weihua Xiao, Venkata Sai Charan Putrevu, Raghu Vamshi Hemadri +2
Prefix circuits are fundamental components in digital adders, widely used in digital systems due to their efficiency in calculating carry signals. Synthesizing prefix circuits with…
Performance analysis of weighted low rank model with sparse image histograms for face recognition under lowlevel illumination and occlusion
K. V. Sridhar, Raghu vamshi Hemadri
In a broad range of computer vision applications, the purpose of Low-rank matrix approximation (LRMA) models is to recover the underlying low-rank matrix from its degraded observat…
AEVB-Comm: An Intelligent CommunicationSystem based on AEVBs
Raghu Vamshi Hemadri, Akshay Rayaluru, Rahul Jashvantbhai Pandya
In recent years, applying Deep Learning (DL) techniques emerged as a common practice in the communication system, demonstrating promising results. The present paper proposes a new…