Implications of the Use of Magnetic Tunnel Junctions as Synapses in Neuromorphic Systems
Pages 317 - 320
Abstract
Spin transfer torque magnetic random access memory (STT-MRAM) is a major breakthrough for embedded and standalone memory applications. Its basic cell, the magnetic tunnel junction, can also be used in a low-energy stochastic regime and implement a "synaptic" function. It can then be the basic element for learning-capable neuromorphic chips that do not separate logic and memory and exploit the magnetic tunnel junctions with an optimum energy efficiency. Implementing this vision, however, raises challenges at the circuit level. Proper addressing of the junctions can perturb their synaptic function. In this work, we investigate several architectures for a system based on stochastic synapses, and compare them in terms of reliability and energy efficiency. These results show the high potential of this technology, and pinpoint some main design challenges and tradeoff.
References
[1]
Devolder, T., Hayakawa, J., Ito, K., Takahashi, H., Ikeda, S., Crozat, P., Zerounian, N., Kim, J.-V., Chappert, C. and Ohno, H. 2008. Single-Shot Time-Resolved Measurements of Nanosecond-Scale Spin-Transfer Induced Switching: Stochastic Versus Deterministic Aspects. Phys. Rev. Lett. 100, 5 (Février 2008), 057206.
[2]
Diao, Z., Li, Z., Wang, S., Ding, Y., Panchula, A., Chen, E., Wang, L.-C. and Huai, Y. 2007. Spin-transfer torque switching in magnetic tunnel junctions and spin-transfer torque random access memory. Journal of Physics: Condensed Matter. 19, 16 (Apr. 2007), 165209.
[3]
Fukushima, A., Seki, T., Yakushiji, K., Kubota, H., Imamura, H., Yuasa, S. and Ando, K. 2014. Spin dice: A scalable truly random number generator based on spintronics. Applied Physics Express. 7, 8 (Aug. 2014), 083001.
[4]
Jo, S.H., Chang, T., Ebong, I., Bhadviya, B.B., Mazumder, P. and Lu, W. 2010. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Lett. 10, 4 (Avril 2010), 1297--1301.
[5]
Kim, S., Ishii, M., Lewis, S., Perri, T., BrightSky, M., Kim, W., Jordan, R., Burr, G.W., Sosa, N., Ray, A., Han, J.P., Miller, C., Hosokawa, K. and Lam, C. 2015. NVM neuromorphic core with 64k-cell (256-by-256) phase change memory synaptic array with on-chip neuron circuits for continuous in-situ learning. 2015 IEEE International Electron Devices Meeting (IEDM) (Dec. 2015), 17.1.1--17.1.4.
[6]
Lakys, Y., Zhao, W.S., Devolder, T., Zhang, Y., Klein, J.-O., Ravelosona, D. and Chappert, C. 2012. Self-Enabled "Error-Free" Switching Circuit for Spin Transfer Torque MRAM and Logic. IEEE Trans. Magn. 48, 9 (2012), 2403--2406.
[7]
Lecerf, G., Tomas, J., Boyn, S., Girod, S., Mangalore, A., Grollier, J. and Saighi, S. 2014. Silicon neuron dedicated to memristive spiking neural networks. 2014 IEEE International Symposium on Circuits and Systems (ISCAS) (Jun. 2014), 1568--1571.
[8]
Prezioso, M., Merrikh-Bayat, F., Hoskins, B.D., Adam, G.C., Likharev, K.K. and Strukov, D.B. 2015. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature. 521, 7550 (May 2015), 61--64.
[9]
Querlioz, D., Bichler, O., Vincent, A.F. and Gamrat, C. 2015. Bioinspired Programming of Memory Devices for Implementing an Inference Engine. Proceedings of the IEEE. 103, 8 (Aug. 2015), 1398--1416.
[10]
Saïghi, S. et al. 2015. Plasticity in memristive devices for spiking neural networks. Neuromorphic Engineering. 9, (2015), 51.
[11]
Sun, J.Z. 2000. Spin-current interaction with a monodomain magnetic body: A model study. Physical Review B. 62, 1 (juillet 2000), 570--578.
[12]
Suri, M., Querlioz, D., Bichler, O., Palma, G., Vianello, E., Vuillaume, D., Gamrat, C. and DeSalvo, B. 2013. Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses. IEEE Transactions on Electron Devices. 60, 7 (2013), 2402--2409.
[13]
Vincent, A.F., Larroque, J., Locatelli, N., Ben Romdhane, N., Bichler, O., Gamrat, C., Zhao, W.S., Klein, J.-O., Galdin-Retailleau, S. and Querlioz, D. 2015. Spin-Transfer Torque Magnetic Memory as a Stochastic Memristive Synapse for Neuromorphic Systems. IEEE Transactions on Biomedical Circuits and Systems. 9, 2 (Apr. 2015), 166--174.
[14]
Vincent, A.F., Locatelli, N., Klein, J.-O., Zhao, W.S., Galdin-Retailleau, S. and Querlioz, D. 2015. Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices. IEEE Transactions on Electron Devices. 62, 1 (Jan. 2015), 164--170.
[15]
Worledge, D.C., Hu, G., Trouilloud, P.L., Abraham, D.W., Brown, S., Gaidis, M.C., Nowak, J., O'Sullivan, E.J., Robertazzi, R.P., Sun, J.Z. and Gallagher, W.J. 2010. Switching distributions and write reliability of perpendicular spin torque MRAM. Electron Devices Meeting (IEDM), 2010 IEEE International (Dec. 2010), 12.5.1--12.5.4.
[16]
Zhao, W., Chappert, C., Javerliac, V. and Noziere, J.P. 2009. High Speed, High Stability and Low Power Sensing Amplifier for MTJ/CMOS Hybrid Logic Circuits. IEEE Transactions on Magnetics. 45, 10 (Oct. 2009), 3784--3787.
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- Implications of the Use of Magnetic Tunnel Junctions as Synapses in Neuromorphic Systems
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May 2017
516 pages
ISBN:9781450349727
DOI:10.1145/3060403
- General Chairs:
- Laleh Behjat,
- Jie Han,
- Program Chairs:
- Miroslav N. Velev,
- Deming Chen
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