9 citations · 21 across the 4 of their papers we have counts for
4 papers
Accelerating Bandwidth-Bound Deep Learning Inference with Main-Memory Accelerators
Benjamin Y. Cho, Jeageun Jung, Mattan Erez
DL inference queries play an important role in diverse internet services and a large fraction of datacenter cycles are spent on processing DL inference queries. Specifically, the m…
Training with Multi-Layer Embeddings for Model Reduction
Benjamin Ghaemmaghami, Zihao Deng, Benjamin Cho +4
Modern recommendation systems rely on real-valued embeddings of categorical features. Increasing the dimension of embedding vectors improves model accuracy but comes at a high cost…
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing
Liu Ke, Udit Gupta, Carole-Jean Wu +18
Personalized recommendation systems leverage deep learning models and account for the majority of data center AI cycles. Their performance is dominated by memory-bound sparse embed…
Near Data Acceleration with Concurrent Host Access
Benjamin Y. Cho, Yongkee Kwon, Sangkug Lym +1
Near-data accelerators (NDAs) that are integrated with main memory have the potential for significant power and performance benefits. Fully realizing these benefits requires the la…