TY - JOUR
T1 - A 40-nm, 64-Kb, 56.67 TOPS/W Voltage-Sensing Computing-In-Memory/Digital RRAM Macro Supporting Iterative Write with Verification and Online Read-Disturb Detection
AU - Yoon, Jong Hyeok
AU - Chang, Muya
AU - Khwa, Win San
AU - Chih, Yu Der
AU - Chang, Meng Fan
AU - Raychowdhury, Arijit
N1 - Publisher Copyright:
© 1966-2012 IEEE.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - Computing-in-memory (CIM) architectures have gained importance in achieving high-throughput energy-efficient artificial intelligence (AI) systems. Resistive RAM (RRAM) is a promising candidate for CIM architectures due to a multiply-and-accumulate (MAC)-friendly structure, high bit density, compatibility with a CMOS process, and nonvolatility. Notwithstanding the advancement of RRAM technology, the reliability of an RRAM array hinders the spread of RRAM applications such that a circuit-technology joint approach is necessary to attain reliable RRAM-based CIM architectures. This article presents a 64-kb hybrid CIM/digital RRAM macro supporting: 1) active-feedback-based voltage-sensing read (RD) to enable 1-8-b programmable vector-matrix multiplication under a low-resistance ratio of the high-resistance state to the low-resistance state in an RRAM array; 2) iterative write with verification to secure a tight resistance distribution; and 3) online RD-disturb detection in the background during CIM. The test chip fabricated in a 40-nm CMOS and RRAM process achieves a peak energy efficiency of 56.67 TOPS/W while demonstrating the eight-bitline hybrid CIM/digital MAC operation with 1-8-b inputs and weights and 20-b outputs without quantization.
AB - Computing-in-memory (CIM) architectures have gained importance in achieving high-throughput energy-efficient artificial intelligence (AI) systems. Resistive RAM (RRAM) is a promising candidate for CIM architectures due to a multiply-and-accumulate (MAC)-friendly structure, high bit density, compatibility with a CMOS process, and nonvolatility. Notwithstanding the advancement of RRAM technology, the reliability of an RRAM array hinders the spread of RRAM applications such that a circuit-technology joint approach is necessary to attain reliable RRAM-based CIM architectures. This article presents a 64-kb hybrid CIM/digital RRAM macro supporting: 1) active-feedback-based voltage-sensing read (RD) to enable 1-8-b programmable vector-matrix multiplication under a low-resistance ratio of the high-resistance state to the low-resistance state in an RRAM array; 2) iterative write with verification to secure a tight resistance distribution; and 3) online RD-disturb detection in the background during CIM. The test chip fabricated in a 40-nm CMOS and RRAM process achieves a peak energy efficiency of 56.67 TOPS/W while demonstrating the eight-bitline hybrid CIM/digital MAC operation with 1-8-b inputs and weights and 20-b outputs without quantization.
KW - Computing-in-memory (CIM)
KW - convolutional neural network (CNN)
KW - multiply-and-accumulate (MAC)
KW - processing-in-memory
KW - read (RD) disturb
KW - resistive RAM (RRAM)
KW - write (WR) verification
UR - https://www.scopus.com/pages/publications/85122288288
U2 - 10.1109/JSSC.2021.3101209
DO - 10.1109/JSSC.2021.3101209
M3 - Article
AN - SCOPUS:85122288288
SN - 0018-9200
VL - 57
SP - 68
EP - 79
JO - IEEE Journal of Solid-State Circuits
JF - IEEE Journal of Solid-State Circuits
IS - 1
ER -