9 citations · 11 across the 3 of their papers we have counts for
4 papers
DPA-MoE: A Runtime Precision Control Framework for MoE Inference
Jaeyun Kim, Hoonki Lee, Susie Shin +3
Mixture-of-Experts (MoE) models reduce computation by routing each token to a small set of experts, but each selected expert is still used at a fixed precision. We present DPA-MoE,…
FlashMoE: Reducing SSD I/O Bottlenecks via ML-Based Cache Replacement for Mixture-of-Experts Inference on Edge Devices
Byeongju Kim, Jungwan Lee, Donghyeon Han +2
Recently, Mixture-of-Experts (MoE) models have gained attention for efficiently scaling large language models. Although these models are extremely large, their sparse activation en…
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…