5 papers
DP-LLM: Runtime Model Adaptation with Dynamic Layer-wise Precision Assignment
Sangwoo Kwon, Seong Hoon Seo, Jae W. Lee +1
How can we effectively handle queries for on-device large language models (LLMs) with varying runtime constraints, such as latency and accuracy? Multi-scale quantization addresses…
NestedFP: High-Performance, Memory-Efficient Dual-Precision Floating Point Support for LLMs
Haeun Lee, Omin Kwon, Yeonhong Park +1
Meeting service-level objectives (SLOs) in Large Language Models (LLMs) serving is critical, but managing the high variability in load presents a significant challenge. Recent adva…
GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance
Jinuk Kim, Marwa El Halabi, Wonpyo Park +5
Post-training quantization is a key technique for reducing the memory and inference latency of large language models by quantizing weights and activations without requiring retrain…
DecDEC: A Systems Approach to Advancing Low-Bit LLM Quantization
Yeonhong Park, Jake Hyun, Hojoon Kim +1
Quantization of Large Language Models (LLMs) has recently gained popularity, particularly for on-device settings with limited hardware resources. While efficient, quantization inev…
A: Accelerating Attention Mechanisms in Neural Networks with Approximation
Tae Jun Ham, Sung Jun Jung, Seonghak Kim +8
With the increasing computational demands of neural networks, many hardware accelerators for the neural networks have been proposed. Such existing neural network accelerators often…