50 citations · 389 across the 39 of their papers we have counts for
8 papers · 1 filter
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…
Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers
Hongjie Wang, Bhishma Dedhia, Niraj K. Jha
Deployment of Transformer models on edge devices is becoming increasingly challenging due to the exponentially growing inference cost that scales quadratically with the number of t…
Im-Promptu: In-Context Composition from Image Prompts
Bhishma Dedhia, Michael Chang, Jake C. Snell +2
Large language models are few-shot learners that can solve diverse tasks from a handful of demonstrations. This implicit understanding of tasks suggests that the attention mechanis…
DOCTOR: A Multi-Disease Detection Continual Learning Framework Based on Wearable Medical Sensors
Chia-Hao Li, Niraj K. Jha
Modern advances in machine learning (ML) and wearable medical sensors (WMSs) in edge devices have enabled ML-driven disease detection for smart healthcare. Conventional ML-driven m…
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…
AccelTran: A Sparsity-Aware Accelerator for Dynamic Inference with Transformers
Shikhar Tuli, Niraj K. Jha
Self-attention-based transformer models have achieved tremendous success in the domain of natural language processing. Despite their efficacy, accelerating the transformer is chall…
ML-FEED: Machine Learning Framework for Efficient Exploit Detection
Tanujay Saha, Tamjid Al-Rahat, Najwa Aaraj +2
Machine learning (ML)-based methods have recently become attractive for detecting security vulnerability exploits. Unfortunately, state-of-the-art ML models like long short-term me…