TY - GEN
T1 - Dynamic Computing in Memory (DCIM) in Resistive Crossbar Arrays
AU - Motaman, Seyedhamidreza
AU - Ghosh, Swaroop
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/16
Y1 - 2019/1/16
N2 - With Von-Neumann computing struggling to match the energy-efficiency of biological systems, there is pressing need to explore alternative computing models. Recent experimental studies have revealed that Resistive Random Access Memory (RRAM) is promising alternative for DRAM. Resistive crossbar arrays possess many promising features that can not only enable high-density and low-power storage but also non Von-Neumann compute models. Most recent works focus on dot product operation with RRAM crossbar arrays, and therefore are not flexible to implement various logical functions. We propose a low-power dynamic computing in memory system which can implement various functions in Sum of Product (SOP) form in RRAM crossbar array architecture. We evaluate the proposed technique by performing simulation over wide range of MCNC benchmarks. Simulation results show 1.42X and 20X latency improvement as well as 2.6X and 12.6X power saving compared to static and MAGIC computing in memory methods.
AB - With Von-Neumann computing struggling to match the energy-efficiency of biological systems, there is pressing need to explore alternative computing models. Recent experimental studies have revealed that Resistive Random Access Memory (RRAM) is promising alternative for DRAM. Resistive crossbar arrays possess many promising features that can not only enable high-density and low-power storage but also non Von-Neumann compute models. Most recent works focus on dot product operation with RRAM crossbar arrays, and therefore are not flexible to implement various logical functions. We propose a low-power dynamic computing in memory system which can implement various functions in Sum of Product (SOP) form in RRAM crossbar array architecture. We evaluate the proposed technique by performing simulation over wide range of MCNC benchmarks. Simulation results show 1.42X and 20X latency improvement as well as 2.6X and 12.6X power saving compared to static and MAGIC computing in memory methods.
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U2 - 10.1109/ICCD.2018.00036
DO - 10.1109/ICCD.2018.00036
M3 - Conference contribution
AN - SCOPUS:85062236974
T3 - Proceedings - 2018 IEEE 36th International Conference on Computer Design, ICCD 2018
SP - 179
EP - 186
BT - Proceedings - 2018 IEEE 36th International Conference on Computer Design, ICCD 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 36th International Conference on Computer Design, ICCD 2018
Y2 - 7 October 2018 through 10 October 2018
ER -
