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.
Since time immemorial, noise has been a constant source of disturbance to the various entities known to mankind. Noise models of different kinds have been developed to study noise in more detailed fashion over the years. Image processing, particularly, has extensively implemented several algorithms to reduce noise in photographs and pictorial documents to alleviate the effect of noise. Images with sparse colours-lesser number of distinct colours in them-are common nowadays, especially in astronomy and astrophysics where black and white colours form the main components. Additive noise of Gaussian type is the most common form of noise to be studied and analysed in majority of communication channels, namely-satellite links, mobile base station to local cellular tower communication channel,et. al. Most of the time, we encounter images from astronomical sources being distorted with noise maximally as they travel long distance from telescopes in outer space to Earth. Considering Additive White Gaussian Noise(AWGN) to be the common noise in these long distance channels, this paper provides an insight and an algorithmic approach to pixel-specific de-noising of sparse-coloured images affected by AWGN. The paper concludes with some essential future avenues and applications of this de-noising method in industry and academia.