Spin-transfer torque magnetic random access memory (STT-MRAM) based computation-in-memory (CIM) architectures have shown great prospects for an energy-efficient computing. However, device variations and non-idealities narrow down the sensing margin that severely impacts the computing accuracy. In this work, we propose an adaptive referencing mechanism to improve the sensing margin of a CIM architecture for boolean binary logic (BBL) operations. We generate reference signals using multiple STT-MRAM devices and place them strategically into the array such that these signals can address the variations and trace the wire parasitics effectively. We have demonstrated this behavior using an STT-MRAM model, which is calibrated using 1Mbit characterized array. Results show that our proposed architecture for binary neural networks (BNN) achieves up to 17.8 TOPS/W on the MNIST dataset and 130× performance improvement for the text encryption compared to the software implementation on Intel Haswell processor.
|Title of host publication||Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||4|
|Publication status||Published - 2022|
|Event||4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022 - Incheon, Korea, Republic of|
Duration: 13 Jun 2022 → 15 Jun 2022
|Name||Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022|
|Conference||4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022|
|Country/Territory||Korea, Republic of|
|Period||13/06/22 → 15/06/22|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- binary logic
- binary neural networks