1 citations · 3 across the 6 of their papers we have counts for
6 papers
SparAMX: Accelerating Compressed LLMs Token Generation on AMX-powered CPUs
Ahmed F. AbouElhamayed, Jordan Dotzel, Yash Akhauri +6
Large language models have high compute, latency, and memory requirements. While specialized accelerators such as GPUs and TPUs typically run these workloads, CPUs are more widely…
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…
Low-Rank Adapters Meet Neural Architecture Search for LLM Compression
J. Pablo Muñoz, Jinjie Yuan, Nilesh Jain
The rapid expansion of Large Language Models (LLMs) has posed significant challenges regarding the computational resources required for fine-tuning and deployment. Recent advanceme…
MultiPruner: Balanced Structure Removal in Foundation Models
J. Pablo Muñoz, Jinjie Yuan, Nilesh Jain
Recently, state-of-the-art approaches for pruning large pre-trained models (LPMs) have demonstrated that the training-free removal of non-critical residual blocks in Transformers i…
Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning
Sixing Yu, J. Pablo Muñoz, Ali Jannesari
Federated learning (FL) offers privacy-preserving decentralized machine learning, optimizing models at edge clients without sharing private data. Simultaneously, foundation models…
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…