504 citations · 577 across the 17 of their papers we have counts for
17 papers
MossNet: Mixture of State-Space Experts is a Multi-Head Attention
Shikhar Tuli, James Seale Smith, Haris Jeelani +5
Large language models (LLMs) have significantly advanced generative applications in natural language processing (NLP). Recent trends in model architectures revolve around efficient…
GraphMERT: Efficient and Scalable Distillation of Reliable Knowledge Graphs from Unstructured Data
Margarita Belova, Jiaxin Xiao, Shikhar Tuli +1
Researchers have pursued neurosymbolic artificial intelligence (AI) applications for nearly three decades. A marriage of the neural and symbolic components can lead to rapid advanc…
FlexiGPT: Pruning and Extending Large Language Models with Low-Rank Weight Sharing
James Seale Smith, Chi-Heng Lin, Shikhar Tuli +5
The rapid proliferation of large language models (LLMs) in natural language processing (NLP) has created a critical need for techniques that enable efficient deployment on memory-c…
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…
DynaMo: Accelerating Language Model Inference with Dynamic Multi-Token Sampling
Shikhar Tuli, Chi-Heng Lin, Yen-Chang Hsu +3
Traditional language models operate autoregressively, i.e., they predict one token at a time. Rapid explosion in model sizes has resulted in high inference times. In this work, we…
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…
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…
CODEBench: A Neural Architecture and Hardware Accelerator Co-Design Framework
Shikhar Tuli, Chia-Hao Li, Ritvik Sharma +1
Recently, automated co-design of machine learning (ML) models and accelerator architectures has attracted significant attention from both the industry and academia. However, most c…
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…
Are Convolutional Neural Networks or Transformers more like human vision?
Shikhar Tuli, Ishita Dasgupta, Erin Grant +1
Modern machine learning models for computer vision exceed humans in accuracy on specific visual recognition tasks, notably on datasets like ImageNet. However, high accuracy can be…
AVAC: A Machine Learning based Adaptive RRAM Variability-Aware Controller for Edge Devices
Shikhar Tuli, Shreshth Tuli
Recently, the Edge Computing paradigm has gained significant popularity both in industry and academia. Researchers now increasingly target to improve performance and reduce energy…
Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges
Sukhpal Singh Gill, Shreshth Tuli, Minxian Xu +15
Cloud computing plays a critical role in modern society and enables a range of applications from infrastructure to social media. Such system must cope with varying load and evolvin…
APEX: Adaptive Ext4 File System for Enhanced Data Recoverability in Edge Devices
Shreshth Tuli, Shikhar Tuli, Udit Jain +1
Recently Edge Computing paradigm has gained significant popularity both in industry and academia. With its increased usage in real-life scenarios, security, privacy and integrity o…
FogBus: A Blockchain-based Lightweight Framework for Edge and Fog Computing
Shreshth Tuli, Redowan Mahmud, Shikhar Tuli +1
The requirement of supporting both latency sensitive and computing intensive Internet of Things (IoT) applications is consistently boosting the necessity for integrating Edge, Fog…