TY - GEN
T1 - Work-in-progress
T2 - 2019 International Conference on Hardware/Software Codesign and System Synthesis, CODES/ISSS 2019
AU - Shivanandamurthy, Supreeth Mysore
AU - Thakkar, Ishan G.
AU - Salehi, Sayed Ahmad
PY - 2019/10/13
Y1 - 2019/10/13
N2 - Stochastic computing based Processing-In-Memory (PIM) architectures (e.g., [1]) can provide massive parallelism with higher energy-efficiency, for implementing complex computations in main memory. However, stochastic computing arithmetic requires random bit streams generated by stochastic number generators (SNGs), which account for significant area and energy consumption. Moreover, SNGs' numerical precision needs improvement to reduce errors in stochastic computations [1]. Thus, low numerical precision and high implementation overheads of SNGs can offset the benefits of adopting stochastic computing in PIM architectures. In this paper, we exploit the inherent stochasticity of Phase Change Memory (PCM) cells to design a scalable and area-energy efficient SNG for PCM-based stochastic PIM architectures. Our designed SNG can achieve up to ~300× lower area and up to ~250× lower energy consumption with better numerical precision, compared to the Linear Feedback Shift Register (LFSR) based conventional SNG from [2].
AB - Stochastic computing based Processing-In-Memory (PIM) architectures (e.g., [1]) can provide massive parallelism with higher energy-efficiency, for implementing complex computations in main memory. However, stochastic computing arithmetic requires random bit streams generated by stochastic number generators (SNGs), which account for significant area and energy consumption. Moreover, SNGs' numerical precision needs improvement to reduce errors in stochastic computations [1]. Thus, low numerical precision and high implementation overheads of SNGs can offset the benefits of adopting stochastic computing in PIM architectures. In this paper, we exploit the inherent stochasticity of Phase Change Memory (PCM) cells to design a scalable and area-energy efficient SNG for PCM-based stochastic PIM architectures. Our designed SNG can achieve up to ~300× lower area and up to ~250× lower energy consumption with better numerical precision, compared to the Linear Feedback Shift Register (LFSR) based conventional SNG from [2].
KW - Phase Change Memory(PCM)
KW - Processing-In-Memory(PIM)
KW - Stochastic Number generator(SNG)
UR - http://www.scopus.com/inward/record.url?scp=85077330692&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077330692&partnerID=8YFLogxK
U2 - 10.1145/3349567.3351717
DO - 10.1145/3349567.3351717
M3 - Conference contribution
AN - SCOPUS:85077330692
T3 - Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis Companion, CODES/ISSS 2019
BT - Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis Companion, CODES/ISSS 2019
Y2 - 13 October 2019 through 18 October 2019
ER -
