3 citations · 3 across the 5 of their papers we have counts for
5 papers
Certifiable Safe RLHF: Semantic Grounding and Fixed Penalty Constraint Optimization for Safer LLM Alignment
Kartik Pandit, Sourav Ganguly, Arnesh Banerjee +2
Ensuring safety is a foundational requirement for large language models (LLMs). Achieving an appropriate balance between enhancing the utility of model outputs and mitigating their…
TPU-Gen: LLM-Driven Custom Tensor Processing Unit Generator
Deepak Vungarala, Mohammed E. Elbtity, Sumiya Syed +5
The increasing complexity and scale of Deep Neural Networks (DNNs) necessitate specialized tensor accelerators, such as Tensor Processing Units (TPUs), to meet various computationa…
LIMCA: LLM for Automating Analog In-Memory Computing Architecture Design Exploration
Deepak Vungarala, Md Hasibul Amin, Pietro Mercati +5
Resistive crossbars enabling analog In-Memory Computing (IMC) have emerged as a promising architecture for Deep Neural Network (DNN) acceleration, offering high memory bandwidth an…
SPICEPilot: Navigating SPICE Code Generation and Simulation with AI Guidance
Deepak Vungarala, Sakila Alam, Arnob Ghosh +1
Large Language Models (LLMs) have shown great potential in automating code generation; however, their ability to generate accurate circuit-level SPICE code remains limited due to a…
SA-DS: A Dataset for Large Language Model-Driven AI Accelerator Design Generation
Deepak Vungarala, Mahmoud Nazzal, Mehrdad Morsali +4
In the ever-evolving landscape of Deep Neural Networks (DNN) hardware acceleration, unlocking the true potential of systolic array accelerators has long been hindered by the daunti…