no code implementations • 23 May 2023 • Donghyuk Kim, Jae-Young Kim, Wontak Han, Jongsoon Won, Haerang Choi, Yongkee Kwon, Joo-Young Kim
In this paper, we propose Darwin, a practical LRDIMM-based multi-level PIM architecture for data analytics, which fully exploits the internal bandwidth of DRAM using the bank-, bank group-, chip-, and rank-level parallelisms.
no code implementations • 29 Oct 2022 • Je Yang, JaeUk Kim, Joo-Young Kim
Unlike supervised model or single-agent reinforcement learning, which actively exploits network pruning, it is obscure that how pruning will work in multi-agent reinforcement learning with its cooperative and interactive characteristics.
no code implementations • 22 Sep 2022 • Seongmin Hong, Seungjae Moon, Junsoo Kim, Sungjae Lee, Minsub Kim, Dongsoo Lee, Joo-Young Kim
DFX is also 8. 21x more cost-effective than the GPU appliance, suggesting that it is a promising solution for text generation workloads in cloud datacenters.
no code implementations • 12 Jul 2022 • Ji-Hoon Kim, Yeo-Reum Park, Jaeyoung Do, Soo-Young Ji, Joo-Young Kim
In this paper, we propose a computational storage platform that can accelerate a large-scale graph-based nearest neighbor search algorithm based on SmartSSD CSD.
no code implementations • 24 Feb 2021 • Je Yang, Seongmin Hong, Joo-Young Kim
In this paper, we present a deep reinforcement learning platform named FIXAR which employs fixed-point data types and arithmetic units for the first time using a SW/HW co-design approach.