TY - GEN
T1 - A review of in-memory computing architectures for machine learning applications
AU - Bavikadi, Sathwika
AU - Sutradhar, Purab Ranjan
AU - Khasawneh, Khaled N.
AU - Ganguly, Amlan
AU - Dinakarrao, Sai Manoj Pudukotai
N1 - Publisher Copyright:
© 2020 Association for Computing Machinery.
PY - 2020/9/7
Y1 - 2020/9/7
N2 - The state-of-the-art traditional computing hardware is struggling to meet the extensive computational load presented by the rapidly growing Machine Learning (ML) and Artificial Intelligence (AI) algorithms such as Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs). In order to obtain hardware solutions to meet the low-latency and high-throughput computational demands from these algorithms, Non-Von Neumann computing architectures such as In-memory Computing (IMC)/ Processing-in-memory (PIM) are being extensively researched and experimented with. In this survey paper, we analyze and review pioneer IMC/PIM works designed to accelerate ML algorithms such as DNNs and CNNs. We investigate different architectural aspects and dimensions of these works and provide our comparative evaluations. Furthermore, we discuss challenges and limitations in IMC research and also present feasible directions based on our observations and insight.
AB - The state-of-the-art traditional computing hardware is struggling to meet the extensive computational load presented by the rapidly growing Machine Learning (ML) and Artificial Intelligence (AI) algorithms such as Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs). In order to obtain hardware solutions to meet the low-latency and high-throughput computational demands from these algorithms, Non-Von Neumann computing architectures such as In-memory Computing (IMC)/ Processing-in-memory (PIM) are being extensively researched and experimented with. In this survey paper, we analyze and review pioneer IMC/PIM works designed to accelerate ML algorithms such as DNNs and CNNs. We investigate different architectural aspects and dimensions of these works and provide our comparative evaluations. Furthermore, we discuss challenges and limitations in IMC research and also present feasible directions based on our observations and insight.
KW - Artificial Intelligence
KW - CNN
KW - DNN
KW - In-memory Computing
KW - Machine learning
KW - Non Von-Neumann Architectures
KW - Processing-in-memory
UR - http://www.scopus.com/inward/record.url?scp=85091319294&partnerID=8YFLogxK
U2 - 10.1145/3386263.3407649
DO - 10.1145/3386263.3407649
M3 - Conference contribution
AN - SCOPUS:85091319294
T3 - Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI
SP - 89
EP - 94
BT - GLSVLSI 2020 - Proceedings of the 2020 Great Lakes Symposium on VLSI
T2 - 30th Great Lakes Symposium on VLSI, GLSVLSI 2020
Y2 - 7 September 2020 through 9 September 2020
ER -