Showing posts with label chip. Show all posts
Showing posts with label chip. Show all posts

Nov 2, 2023

[paper] ChipNeMo

Mingjie Liu, Teo Ene, Robert Kirby, Chris Cheng, Nathaniel Pinckney, Rongjian LiangJonah 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, Brucek Khailany Kishor Kunal, Xiaowei Li, Hao Liu, Stuart Oberman, Sujeet Omar, Sreedhar Pratty, Ambar Sarkar Zhengjiang Shao, Hanfei Sun, Pratik P Suthar, Varun Tej, Kaizhe Xu, Haoxing Ren
ChipNeMo: Domain-Adapted LLMs for Chip Design
arXiv:2311.00176 [cs.CL]
DOI: 10.48550/arXiv.2311.00176

* NVIDIA

Abstract: ChipNeMo aims to explore the applications of large language models (LLMs) for industrial chip design. Instead of directly deploying off-the-shelf commercial or open-source LLMs, we instead adopt the following domain adaptation techniques: custom tokenizers, domain-adaptive continued pretraining, supervised fine-tuning (SFT) with domain-specific instructions, and domain-adapted retrieval models. We evaluate these methods on three selected LLM applications for chip design: an engineering assistant chatbot, EDA script generation, and bug summarization and analysis. Our results show that these domain adaptation techniques enable significant LLM performance improvements over general-purpose base models across the three evaluated applications, enabling up to 5x model size reduction with similar or better performance on a range of design tasks. Our findings also indicate that there’s still room for improvement between our current results and ideal outcomes. We believe that further investigation of domain-adapted LLM approaches will help close this gap in the future.
Fig: LLM script generator integration with EDA tools

Acknowledgements: The authors would like to thank: NVIDIA IT teams for their support on NVBugs integration; NVIDIA Hardware Security team for their support on security issues; NVIDIA NeMo teams for their support and guidance on training and inference of ChipNeMo models; NVIDIA Infrastructure teams for supporting the GPU training and inference resources for the project; NVIDIA Hardware design teams for their support and insight.

Feb 3, 2022

[paper] Piezosensitive Pressure Sensor Chip

Mikhail Basov
Pressure sensor chip utilizing electrical circuit of piezosensitive differential amplifier with negative feedback loop (PDA-NFL) for 5 kPa
XI International Scientific and Technical Conference 
"Micro-, and Nanotechnology in Electronics", 
Elbrus, Russia; June 2021
  
Dukhov Automatics Research Institute VNIIA, Moscow

Abstract: High sensitive (S=11.2±1.8 mV/V/kPa with nonlinearity error 2KNL=0.15±0.09 % /FS) small-sized (4.00x4.00 mm2) silicon pressure sensor chip utilizing new electrical circuit for microelectromechanical systems (MEMS) in the form of differential amplifier with negative feedback loop (PDA-NFL) for 5 kPa differential was developed. The advantages are demonstrated in the array of output characteristics, which prove the relevance of the presented development, relative to modern developments of pressure sensors with Wheatstone bridge electrical circuit for 5 kPa range.

Fig: a) Pressure sensor chip, b) its assembled structure




Feb 21, 2017

1-cent "lab on a chip" could save lives

Rahim Esfandyarpour, Stanford University, helped to develop a way to create a diagnostic "lab on a chip" for just a penny:
"I'm pretty sure it will open a window for researchers because it makes life much easier for them - just print it and use it," said Esfandyarpour. The results of this research were recently published in the journal Proceedings of the National Academy of Sciences [Source: Stanford Medicine]