ChipNeMo: Domain-Adapted LLMs for Chip Design

no code implementations • 31 Oct 2023 • Mingjie Liu, Teodor-Dumitru Ene, Robert Kirby, Chris Cheng, Nathaniel Pinckney, Rongjian Liang, Jonah Alben, Himyanshu Anand, Sanmitra Banerjee, Ismet Bayraktaroglu, Bonita Bhaskaran, Bryan Catanzaro, Arjun Chaudhuri, Sharon Clay, Bill Dally, Laura Dang, Parikshit Deshpande, Siddhanth Dhodhi, Sameer Halepete, Eric Hill, Jiashang Hu, Sumit Jain, Ankit Jindal, Brucek Khailany, George Kokai, Kishor Kunal, Xiaowei Li, Charley Lind, Hao liu, Stuart Oberman, Sujeet Omar, Ghasem Pasandi, Sreedhar Pratty, Jonathan Raiman, Ambar Sarkar, Zhengjiang Shao, Hanfei Sun, Pratik P Suthar, Varun Tej, Walker Turner, Kaizhe Xu, Haoxing Ren

ChipNeMo aims to explore the applications of large language models (LLMs) for industrial chip design.

Chatbot Domain Adaptation +2

AISYN: AI-driven Reinforcement Learning-Based Logic Synthesis Framework

no code implementations • 8 Feb 2023 • Ghasem Pasandi, Sreedhar Pratty, James Forsyth

This is because AI and RL can help minimizing QoR further by exiting from local minima.

reinforcement-learning Reinforcement Learning (RL)

Deep-PowerX: A Deep Learning-Based Framework for Low-Power Approximate Logic Synthesis

1 code implementation • 3 Jul 2020 • Ghasem Pasandi, Mackenzie Peterson, Moises Herrera, Shahin Nazarian, Massoud Pedram

This paper aims at integrating three powerful techniques namely Deep Learning, Approximate Computing, and Low Power Design into a strategy to optimize logic at the synthesis level.

Approximate Logic Synthesis: A Reinforcement Learning-Based Technology Mapping Approach

no code implementations • 1 Feb 2019 • Ghasem Pasandi, Shahin Nazarian, Massoud Pedram

Approximate Logic Synthesis (ALS) is the process of synthesizing and mapping a given Boolean network to a library of logic cells so that the magnitude/rate of error between outputs of the approximate and initial (exact) Boolean netlists is bounded from above by a predetermined total error threshold.

Hardware Architecture

NullaNet: Training Deep Neural Networks for Reduced-Memory-Access Inference

no code implementations • 23 Jul 2018 • Mahdi Nazemi, Ghasem Pasandi, Massoud Pedram

Deep neural networks have been successfully deployed in a wide variety of applications including computer vision and speech recognition.

speech-recognition Speech Recognition