504 citations · 577 across the 17 of their papers we have counts for
6 papers · 1 filter
MoDeGPT: Modular Decomposition for Large Language Model Compression
Chi-Heng Lin, Shangqian Gao, James Seale Smith +5
Large Language Models (LLMs) have reshaped the landscape of artificial intelligence by demonstrating exceptional performance across various tasks. However, substantial computationa…
BREATHE: Second-Order Gradients and Heteroscedastic Emulation based Design Space Exploration
Shikhar Tuli, Niraj K. Jha
Researchers constantly strive to explore larger and more complex search spaces in various scientific studies and physical experiments. However, such investigations often involve so…
TransCODE: Co-design of Transformers and Accelerators for Efficient Training and Inference
Shikhar Tuli, Niraj K. Jha
Automated co-design of machine learning models and evaluation hardware is critical for efficiently deploying such models at scale. Despite the state-of-the-art performance of trans…
EdgeTran: Co-designing Transformers for Efficient Inference on Mobile Edge Platforms
Shikhar Tuli, Niraj K. Jha
Automated design of efficient transformer models has recently attracted significant attention from industry and academia. However, most works only focus on certain metrics while se…
FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid?
Shikhar Tuli, Bhishma Dedhia, Shreshth Tuli +1
The existence of a plethora of language models makes the problem of selecting the best one for a custom task challenging. Most state-of-the-art methods leverage transformer-based m…
Generative Optimization Networks for Memory Efficient Data Generation
Shreshth Tuli, Shikhar Tuli, Giuliano Casale +1
In standard generative deep learning models, such as autoencoders or GANs, the size of the parameter set is proportional to the complexity of the generated data distribution. A sig…