Search Results for author:
Found 5 papers, 3 papers with code
LLMem: Estimating GPU Memory Usage for Fine-Tuning Pre-Trained LLMs
Fine-tuning pre-trained large language models (LLMs) with limited hardware presents challenges due to GPU memory constraints.
Tensor Slicing and Optimization for Multicore NPUs
Although code generation for Convolution Neural Network (CNN) models has been extensively studied, performing efficient data slicing and parallelization for highly-constrai\-ned Multicore Neural Processor Units (NPUs) is still a challenging problem.
Q-HyViT: Post-Training Quantization of Hybrid Vision Transformers with Bridge Block Reconstruction for IoT Systems
For mobile devices, achieving optimal acceleration for ViTs necessitates the strategic integration of quantization techniques and efficient hybrid transformer structures.
CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution
We propose CPrune, a compiler-informed model pruning for efficient target-aware DNN execution to support an application with a required target accuracy.
Quantune: Post-training Quantization of Convolutional Neural Networks using Extreme Gradient Boosting for Fast Deployment
To adopt convolutional neural networks (CNN) for a range of resource-constrained targets, it is necessary to compress the CNN models by performing quantization, whereby precision representation is converted to a lower bit representation.
