21 citations · 44 across the 6 of their papers we have counts for
7 papers
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…
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…