Time-Domain Computing in Memory Using Spintronics for Energy-Efficient Convolutional Neural Network
| Autor: | Youguang Zhang, Yining Bai, Guanda Wang, Kun Zhang, Lei Chen, Georgios Ch. Sirakoulis, Yue Zhang, Zhenyi Zheng, Chenyu Lian, Jinkai Wang, Zhizhong Zhang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Adder
Computer science 020208 electrical & electronic engineering ComputerApplications_COMPUTERSINOTHERSYSTEMS 02 engineering and technology Convolutional neural network Bottleneck Computational science symbols.namesake 0202 electrical engineering electronic engineering information engineering symbols Time domain Electrical and Electronic Engineering Energy (signal processing) MNIST database Von Neumann architecture Data transmission |
| Zdroj: | IEEE Transactions on Circuits and Systems I: Regular Papers. 68:1193-1205 |
| ISSN: | 1558-0806 1549-8328 |
| Popis: | The data transfer bottleneck in Von Neumann architecture owing to the separation between processor and memory hinders the development of high-performance computing. The computing in memory (CIM) concept is widely considered as a promising solution for overcoming this issue. In this article, we present a time-domain CIM (TD-CIM) scheme using spintronics, which can be applied to construct the energy-efficient convolutional neural network (CNN). Basic Boolean logic operations are implemented through recording the bit-line output at different moments. A multi-addend addition mechanism is then introduced based on the TD-CIM circuit, which can eliminate the cascaded full adders. To further optimize the compatibility of TD-CIM circuit for CNN, we also propose a quantization method that transforms floating-point parameters of pre-trained CNN models into fixed-point parameters. Finally, we build a TD-CIM architecture integrating with a highly reconfigurable array of field-free spin-orbit torque magnetic random access memory (SOT-MRAM) and evaluate its benefits for the quantized CNN. By performing digit recognition with the MNIST dataset, we find that the delay and energy are respectively reduced by 1.2-2.7 times and $2.4\times 10 ^{3} - 1.1\times 10 ^{4}$ times compared with STT-CIM and CRAM based on spintronic memory. Finally, the recognition accuracy can reach 98.65% and 91.11% on MNIST and CIFAR-10, respectively. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|