hc34.SKhynix.YongkeeKwon.v03.pdf

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1、System Architecture and Software Stack for GDDR6-AiMYongkee Kwon,Kornijcuk Vladimir,Nahsung Kim,Woojae Shin,Jongsoon Won,Minkyu Lee,Hyunha Joo,Haerang Choi,Guhyun Kim,ByeongjuAn,Jeongbin Kim,Jaewook Lee,Ilkon Kim,Jaehan Park,Chanwook Park,Yosub Song,Byeongsu Yang,Hyungdeok Lee,Seho Kim,Daehan Kwon,S

2、eongju Lee,Kyuyoung Kim,Sanghoon Oh,Joonhong Park,Gimoon Hong,Dongyoon Ka,Kyudong Hwang,Jeongje Park,Kyeongpil Kang,Jungyeon Kim,Junyeol Jeon,Myeongjun Lee,Minyoung Shin,Minhwan Shin,Jaekyung Cha,Changson Jung,Kijoon Chang,Chunseok Jeong,Euicheol Lim,Il Park,and Junhyun Chun,SK hynix SK hynix Inc.Th

3、is material is proprietary of SK hynix Inc.and subject to change without notice./Confidential Text AbstractThis poster presents system architecture,software stack,and performance analysis for SK hynixs very first GDDR6-based processing-in-memory(PIM)product sample,called Accelerator-in-Memory(AiM).A

4、iM is designed for the in-memory acceleration of matrix-vector product operations,which are commonly found in machine learning applications.The strength of AiM primarily comes from the two design factors,which are 1)all-bank operation support and 2)extended DRAM command set.All-bank operations allow

5、 AiM to fully utilize the abundant internal DRAM bandwidth,which makes it an attractive solution for memory-bound applications.The extended command set allows the host to address these new operations efficiently and provides a clean separation of concerns between the AiM architecture and its softwar

6、e stack design.We present a dedicated FPGA-based reference platform with a software stack,which is used to validate AiM design and evaluate its system-level performance.We also demonstrate FMC-based AiM extension cards that are compatible with the off-the-shelf FPGA boards and serve as an open resea