16 citations · 27 across the 8 of their papers we have counts for
6 papers · 1 filter
Mamba-Shedder: Post-Transformer Compression for Efficient Selective Structured State Space Models
J. Pablo Muñoz, Jinjie Yuan, Nilesh Jain
Large pre-trained models have achieved outstanding results in sequence modeling. The Transformer block and its attention mechanism have been the main drivers of the success of thes…
Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models
Sixing Yu, J. Pablo Muñoz, Ali Jannesari
Foundation Models (FMs), such as LLaMA, BERT, GPT, ViT, and CLIP, have demonstrated remarkable success in a wide range of applications, driven by their ability to leverage vast amo…
EZNAS: Evolving Zero Cost Proxies For Neural Architecture Scoring
Yash Akhauri, J. Pablo Munoz, Nilesh Jain +1
Neural Architecture Search (NAS) has significantly improved productivity in the design and deployment of neural networks (NN). As NAS typically evaluates multiple models by trainin…
A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities
Daniel Cummings, Anthony Sarah, Sharath Nittur Sridhar +3
Recent advances in Neural Architecture Search (NAS) such as one-shot NAS offer the ability to extract specialized hardware-aware sub-network configurations from a task-specific sup…
Enabling NAS with Automated Super-Network Generation
J. Pablo Muñoz, Nikolay Lyalyushkin, Yash Akhauri +3
Recent Neural Architecture Search (NAS) solutions have produced impressive results training super-networks and then deriving subnetworks, a.k.a. child models that outperform expert…
RHNAS: Realizable Hardware and Neural Architecture Search
Yash Akhauri, Adithya Niranjan, J. Pablo Muñoz +6
The rapidly evolving field of Artificial Intelligence necessitates automated approaches to co-design neural network architecture and neural accelerators to maximize system efficien…