64 citations · 69 across the 6 of their papers we have counts for
6 papers
From Arithmetic to Logic: The Resilience of Logic and Lookup-Based Neural Networks Under Parameter Bit-Flips
Alan T. L. Bacellar, Sathvik Chemudupati, Shashank Nag +4
The deployment of deep neural networks (DNNs) in safety-critical edge environments necessitates robustness against hardware-induced bit-flip errors. While empirical studies indicat…
LL-ViT: Edge Deployable Vision Transformers with Look Up Table Neurons
Shashank Nag, Alan T. L. Bacellar, Zachary Susskind +9
Vision Transformers have been tremendously successful in computer vision tasks. However, their large computational, memory, and energy demands are a challenge for edge inference on…
HELIOS: Adaptive Model And Early-Exit Selection for Efficient LLM Inference Serving
Avinash Kumar, Shashank Nag, Jason Clemons +2
Early-Exit Large Language Models (EE-LLMs) enable high throughput inference by allowing tokens to exit early at intermediate layers. However, their throughput is limited by the com…
nanoML for Human Activity Recognition
Alan T. L. Bacellar, Mugdha P. Jadhao, Shashank Nag +3
Human Activity Recognition (HAR) is critical for applications in healthcare, fitness, and IoT, but deploying accurate models on resource-constrained devices remains challenging due…
Shrinking the Giant : Quasi-Weightless Transformers for Low Energy Inference
Shashank Nag, Alan T. L. Bacellar, Zachary Susskind +9
Transformers are set to become ubiquitous with applications ranging from chatbots and educational assistants to visual recognition and remote sensing. However, their increasing com…
ViTA: A Vision Transformer Inference Accelerator for Edge Applications
Shashank Nag, Gourav Datta, Souvik Kundu +2
Vision Transformer models, such as ViT, Swin Transformer, and Transformer-in-Transformer, have recently gained significant traction in computer vision tasks due to their ability to…