9 citations · 19 across the 8 of their papers we have counts for
8 papers
A.X K2 Technical Report
Cheolseung Baek, Dhammiko Arya, Eunki Kim +40
We introduce A.X K2, a 688B-parameter Mixture-of-Experts (MoE) language model trained from scratch as a high-performance foundation for \emph{agentic} applications. Trained on appr…
LightRot: A Light-Weighted Rotation Scheme and Architecture for Accurate Low-Bit Large Language Model Inference
Sangjin Kim, Yuseon Choi, Jungjun Oh +2
As large language models (LLMs) continue to demonstrate exceptional capabilities across various domains, the challenge of achieving energy-efficient and accurate inference becomes…
GyRot: Leveraging Hidden Synergy between Rotation and Fine-grained Group Quantization for Low-bit LLM Inference
Sangjin Kim, Yuseon Choi, Byeongcheol Kim +2
Low-bit quantization is essential for efficient LLM inference, and both rotation and fine-grained group quantization have shown individual promise. However, their combination often…
ELMoE-3D: Leveraging Intrinsic Elasticity of MoE for Hybrid-Bonding-Enabled Self-Speculative Decoding in On-Premises Serving
Yuseon Choi, Jingu Lee, Jungjun Oh +5
Mixture-of-Experts (MoE) models have become the dominant architecture for large-scale language models, yet on-premises serving remains fundamentally memory-bound as batching turns…
SeVeDo: A Heterogeneous Transformer Accelerator for Low-Bit Inference via Hierarchical Group Quantization and SVD-Guided Mixed Precision
Yuseon Choi, Sangjin Kim, Jungjun Oh +2
Low-bit quantization is a promising technique for efficient transformer inference by reducing computational and memory overhead. However, aggressive bitwidth reduction remains chal…
SliceMoE: Bit-Sliced Expert Caching under Miss-Rate Constraints for Efficient MoE Inference
Yuseon Choi, Sangjin Kim, Jungjun Oh +3
MoE models offer efficient scaling through conditional computation, but their large parameter size and expensive expert offloading make on-device deployment challenging. Existing a…
Two-Step Spike Encoding Scheme and Architecture for Highly Sparse Spiking-Neural-Network
Sangyeob Kim, Sangjin Kim, Soyeon Um +2
This paper proposes a two-step spike encoding scheme, which consists of the source encoding and the process encoding for a high energy-efficient spiking-neural-network (SNN) accele…
GST: Group-Sparse Training for Accelerating Deep Reinforcement Learning
Juhyoung Lee, Sangyeob Kim, Sangjin Kim +2
Deep reinforcement learning (DRL) has shown remarkable success in sequential decision-making problems but suffers from a long training time to obtain such good performance. Many pa…