2 citations · 4 across the 4 of their papers we have counts for
4 papers
Agent-X: Full Pipeline Acceleration of On-device AI Agents
Jinha Chung, Byeongjun Shin, Jiin Kim +1
LLM-based agents deliver state-of-the-art performance across tasks but incur high end-to-end latency on edge devices. We introduce Agent-X, a software-only, accuracy-preserving fra…
SpecMoE: A Fast and Efficient Mixture-of-Experts Inference via Self-Assisted Speculative Decoding
Jehyeon Bang, Eunyeong Cho, Ranggi Hwang +2
The Mixture-of-Experts (MoE) architecture has emerged as a promising approach to mitigate the rising computational costs of large language models (LLMs) by selectively activating p…
The Cost of Dynamic Reasoning: Demystifying AI Agents and Test-Time Scaling from an AI Infrastructure Perspective
Jiin Kim, Byeongjun Shin, Jinha Chung +1
Large-language-model (LLM)-based AI agents have recently showcased impressive versatility by employing dynamic reasoning, an adaptive, multi-step process that coordinates with exte…
SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures
Yunjae Lee, Jinha Chung, Minsoo Rhu
Graph neural networks (GNNs) can extract features by learning both the representation of each objects (i.e., graph nodes) and the relationship across different objects (i.e., the e…