5 papers
F-BFQ: Flexible Block Floating-Point Quantization Accelerator for LLMs
Jude Haris, José Cano
Large Language Models (LLMs) have become increasingly prominent for daily tasks, from improving sound-totext translation to generating additional frames for the latest video games.…
Accelerating Transposed Convolutions on FPGA-based Edge Devices
Jude Haris, José Cano
Transposed Convolutions (TCONV) enable the up-scaling mechanism within generative Artificial Intelligence (AI) models. However, the predominant Input-Oriented Mapping (IOM) method…
ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks
Wenhao Hu, Paul Henderson, José Cano
Pruning is a widely used method for compressing Deep Neural Networks (DNNs), where less relevant parameters are removed from a DNN model to reduce its size. However, removing param…
Quantitative Analysis of Deeply Quantized Tiny Neural Networks Robust to Adversarial Attacks
Idris Zakariyya, Ferheen Ayaz, Mounia Kharbouche-Harrari +4
Reducing the memory footprint of Machine Learning (ML) models, especially Deep Neural Networks (DNNs), is imperative to facilitate their deployment on resource-constrained edge dev…
DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations
Wenhao Hu, Paul Henderson, José Cano
Quantization of Deep Neural Network (DNN) activations is a commonly used technique to reduce compute and memory demands during DNN inference, which can be particularly beneficial o…