3 citations · 3 across the 4 of their papers we have counts for
6 papers
PICasso: An AI-Enabled Design Framework for Autonomous Optimization of Silicon Photonic Devices
Deepak Vungarala, Deniz Najafi, Abdulrahman Aljoudi +6
We present PICasso, an AI-assisted framework for automated synthesis, verification, and optimization of photonic integrated circuits (PICs) from natural-language specifications. PI…
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…
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…