4 papers
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…
FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design
Mahmoud Nazzal, Khoa Nguyen, Deepak Vungarala +4
AI hardware design is advancing rapidly, driven by the promise of design automation to make chip development faster, more efficient, and more accessible to a wide range of users. A…
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…
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…