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A 1ynm 1.25V 8gb 16gb/s/pin GDDR6-Based Accelerator-in-Memory Supporting 1TFLOPS MAC Operation and Various Activation Functions for Deep Learning Application

Daehan Kwon,Seongju Lee,Kyuyoung Kim,Sanghoon Oh,Joonhong Park,Gi-Moon Hong,Dongyoon Ka,Kyudong Hwang,Jeongje Park,Kyeongpil Kang,Jungyeon Kim,Junyeol Jeon,Nahsung Kim,Yongkee Kwon,Vladimir Kornijcuk,Woojae Shin,Jongsoon Won,Minkyu Lee,Hyunha Joo,Haerang Choi,Guhyun Kim,Byeongju An,Jaewook Lee,Donguc Ko,Younggun Jun,Ilwoong Kim,Choungki Song,Ilkon Kim,Chanwook Park,Seho Kim,Chunseok Jeong,Euicheol Lim, Dongkyun Kim,Jieun Jang,Il Park,Junhyun Chun,Joohwan Cho

IEEE Journal of Solid-State Circuits（2022）

Cited 31|Views45
Key words
Random access memory,Adders,Microprocessors,Deep learning,Costs,Bandwidth,Throughput,Accelerator-in-memory (AiM),activation function (AF),adder tree,bank-wide mantissa shift (BWMS),dynamic random access memory (DRAM),GDDR6,multiply-accumulate (MAC),processing-in-memory (PIM)
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