18 citations · 44 across the 7 of their papers we have counts for
7 papers · 1 filter
Efficient Reasoning on the Edge
Yelysei Bondarenko, Thomas Hehn, Rob Hesselink +15
Large language models (LLMs) with chain-of-thought reasoning achieve state-of-the-art performance across complex problem-solving tasks, but their verbose reasoning traces and large…
FPTQuant: Function-Preserving Transforms for LLM Quantization
Boris van Breugel, Yelysei Bondarenko, Paul Whatmough +1
Large language models (LLMs) require substantial compute, and thus energy, at inference time. While quantizing weights and activations is effective at improving efficiency, naive q…
Low-Rank Quantization-Aware Training for LLMs
Yelysei Bondarenko, Riccardo Del Chiaro, Markus Nagel
Large language models (LLMs) are omnipresent, however their practical deployment is challenging due to their ever increasing computational and memory demands. Quantization is one o…
Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing
Yelysei Bondarenko, Markus Nagel, Tijmen Blankevoort
Transformer models have been widely adopted in various domains over the last years, and especially large language models have advanced the field of AI significantly. Due to their s…
Overcoming Oscillations in Quantization-Aware Training
Markus Nagel, Marios Fournarakis, Yelysei Bondarenko +1
When training neural networks with simulated quantization, we observe that quantized weights can, rather unexpectedly, oscillate between two grid-points. The importance of this eff…
Understanding and Overcoming the Challenges of Efficient Transformer Quantization
Yelysei Bondarenko, Markus Nagel, Tijmen Blankevoort
Transformer-based architectures have become the de-facto standard models for a wide range of Natural Language Processing tasks. However, their memory footprint and high latency are…
A White Paper on Neural Network Quantization
Markus Nagel, Marios Fournarakis, Rana Ali Amjad +3
While neural networks have advanced the frontiers in many applications, they often come at a high computational cost. Reducing the power and latency of neural network inference is…