Abstract
Spin transfer torque MRAM (STT-MRAM) based digital in-memory computing (IMC) has been recently proposed for energy efficient processing of convolutional neural network (CNN). The conventional MRAM based IMC architecture suffers from excessive storage area since a large number of intermediate sum and carry bits should be stored for the following successive additions during multiply-accumulate (MAC) operations. In this paper, we propose an area efficient bi-directional MRAM digital IMC (BiMDiM) scheme, where the size of memory cells storing the intermediate sums and carries can be efficiently reduced by repetitively using the same memory cells during MAC operations. In addition, to reduce the number of inefficient half-additions, which can process only two inputs with almost same hardware cost, the addition re-scheduling is also presented to further improve the energy and latency of BiMDiM. The proposed BiMDiM architecture has been simulated using 28nm CMOS process. When compared to the baseline architecture, the proposed BiMDiM improves area efficiency up to 53%.
Original language | English |
---|---|
Title of host publication | Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 74-77 |
Number of pages | 4 |
ISBN (Electronic) | 9781665409964 |
DOIs | |
Publication status | Published - 2022 |
Event | 4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022 - Incheon, Korea, Republic of Duration: 2022 Jun 13 → 2022 Jun 15 |
Publication series
Name | Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022 |
---|
Conference
Conference | 4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022 |
---|---|
Country/Territory | Korea, Republic of |
City | Incheon |
Period | 22/6/13 → 22/6/15 |
Bibliographical note
Funding Information:This work was supported in part by National R&D Program through the National Research Foundation of Korea funded by Ministry of Science and ICT (NRF-2020M3F3A2A01082591), and in part by the National Research Foundation of Korea grant funded by the Korea government (NRF-2020R1A2C3014820). The EDA tool was supported by the IC Design Education Center(IDEC), Korea.
Publisher Copyright:
© 2022 IEEE.
Keywords
- Convolutional neural network (CNN)
- digital in memory computing (digital IMC)
- Memory area
- Spin Transfer Torque magnetic random access memory (STT-MRAM)
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Computer Vision and Pattern Recognition
- Hardware and Architecture
- Human-Computer Interaction
- Electrical and Electronic Engineering