8 citations · 9 across the 4 of their papers we have counts for
4 papers
GradPIM: A Practical Processing-in-DRAM Architecture for Gradient Descent
Heesu Kim, Hanmin Park, Taehyun Kim +6
In this paper, we present GradPIM, a processing-in-memory architecture which accelerates parameter updates of deep neural networks training. As one of processing-in-memory techniqu…
Autoencoder-Based Incremental Class Learning without Retraining on Old Data
Euntae Choi, Kyungmi Lee, Kiyoung Choi
Incremental class learning, a scenario in continual learning context where classes and their training data are sequentially and disjointedly observed, challenges a problem widely k…
Network Recasting: A Universal Method for Network Architecture Transformation
Joonsang Yu, Sungbum Kang, Kiyoung Choi
This paper proposes network recasting as a general method for network architecture transformation. The primary goal of this method is to accelerate the inference process through th…
Resource Sharing and Pipelining in Coarse-Grained Reconfigurable Architecture for Domain-Specific Optimization
Yoonjin Kim, Mary Kiemb, Chulsoo Park +2
Coarse-grained reconfigurable architectures aim to achieve both goals of high performance and flexibility. However, existing reconfigurable array architectures require many resourc…