TY - GEN
T1 - Performance Analysis of a Phase-Change Memory System on Various CNN Inference Workloads
AU - Jang, Jihoon
AU - Kim, Hyun
AU - Lee, Hyokeun
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper, we analyze the suitability of convolutional neural network (CNN) inference workloads on a phase-change memory (PCM) platform. CNN inference has an average of 14\times more read requests than write requests (i.e., read dominant) and a significantly low last-level cache misses per kilo instructions (LLC MPKI) of 2 on average (i.e., computation intensive). In addition, to compare the latency and energy of PCM and DRAM systems, we evaluate CNN inference workloads on two memory systems through a memory simulator. As a result, compared to DRAM, PCM can save total energy by 54% on average, but instruction per cycle (IPC) of PCM is reduced by an average of 28%. In conclusion, CNN inference is a workload suitable for PCM in terms of energy efficiency, but it must be accompanied by a scheme to improve IPC for practical use.
AB - In this paper, we analyze the suitability of convolutional neural network (CNN) inference workloads on a phase-change memory (PCM) platform. CNN inference has an average of 14\times more read requests than write requests (i.e., read dominant) and a significantly low last-level cache misses per kilo instructions (LLC MPKI) of 2 on average (i.e., computation intensive). In addition, to compare the latency and energy of PCM and DRAM systems, we evaluate CNN inference workloads on two memory systems through a memory simulator. As a result, compared to DRAM, PCM can save total energy by 54% on average, but instruction per cycle (IPC) of PCM is reduced by an average of 28%. In conclusion, CNN inference is a workload suitable for PCM in terms of energy efficiency, but it must be accompanied by a scheme to improve IPC for practical use.
KW - convolution neural networks
KW - memory simulation
KW - non-volatile memory
KW - Phase-change memory
UR - http://www.scopus.com/inward/record.url?scp=85148451120&partnerID=8YFLogxK
U2 - 10.1109/ISOCC56007.2022.10031496
DO - 10.1109/ISOCC56007.2022.10031496
M3 - Conference contribution
AN - SCOPUS:85148451120
T3 - Proceedings - International SoC Design Conference 2022, ISOCC 2022
SP - 133
EP - 134
BT - Proceedings - International SoC Design Conference 2022, ISOCC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th International System-on-Chip Design Conference, ISOCC 2022
Y2 - 19 October 2022 through 22 October 2022
ER -
