Neural network detector with sparse codes for spin transfer torque magnetic random access memory

Figures & data

Figure 1. STT-MRAM cell structure. a) 1 to 0 transition. b) 0 to 1 transition.

Figure 2. Cascaded STT-MRAM model, for reading with write-0 direction. WEM: write error model; REM: read disturb error model; GMC: Gaussian mixture channel.

Figure 3. Block diagram of system model.

Figure 4. Proposed architecture of MLP model.

Figure 5. BER of the MLP-based detector for each epoch during training.

Table 1. Parameters of the proposed MLP architecture

Network parameter	8190
Training samples	$1\times {10}^5\mathit{N}$
Epoch	30
Mini-batch size	$6\times {\mathit{N}}^2$
Cost function	MSE
Initializer	Xavier
Optimizer	Adam

Figure 6. BER comparison without offset.

Figure 7. BER comparison, offset of ${\mu }_{\mathit{ofs}}=-0.2\mathit{k\Omega }$ and ${\sigma }_{\mathit{ofs}}/{\mu }_1=4\mathit{\%}$.

Figure 8. BER comparison, offset of ${\mu }_{\mathit{ofs}}=-0.2\mathit{k\Omega }$ and ${\mathit{\sigma }}_{\mathit{ofs}}/{\mathit{\mu }}_1=4\mathit{\%}$.

Table 2. BER comparison for offset of ${\mu }_{\mathit{ofs}}=-0.2\mathit{k}\mathrm{\Omega }$ and ${\sigma }_{\mathit{ofs}}/{\mu }_1=4\mathrm{\%}$ between pearson-based decoding and Euclidean-based decoding

${\sigma }_0/{\mu }_0$	Models	Pearson-based decoding	Euclidean-based decoding
$7\mathrm{\%}$	7/9 code, wo MLP, detector output	0.012039296296	0.012032617284
	7/9 code, wo MLP, decoder output	0.003201603175	0.046494126984
$8\mathrm{\%}$	7/9 code, wo MLP, detector output	0.017924827160	0.017900432099
	7/9 code, wo MLP, decoder output	0.007607619048	0.067908968254
$9\mathrm{\%}$	7/9 code, wo MLP, detector output	0.024453555556	0.024437370370
	7/9 code, wo MLP, decoder output	0.014909571429	0.090837095238
$10\mathrm{\%}$	7/9 code, wo MLP, detector output	0.031314839506	0.031311098765
	7/9 code, wo MLP, decoder output	0.025180126984	0.113879428571

Table 3. BER comparison between the proposed model and ref [27] at offset of ${\mu }_{\mathit{ofs}}=-0.2\mathit{k}\mathrm{\Omega }$ and ${\sigma }_{\mathit{ofs}}/{\mu }_1=4\mathrm{\%}$

${\sigma }_0/{\mu }_0\left[\mathrm{\%}\right]$	Proposed model	Model in reference (Mei et al., 2019)
6	~ ${10}^{-3.9}$	~ ${10}^{-4}$
7	~ ${10}^{-3.75}$	~ ${10}^{-3.6}$
8	~ ${10}^{-3.4}$	~ ${10}^{-3}$
9	~ ${10}^{-2.95}$	~ ${10}^{-2.7}$
10	~ ${10}^{-2.6}$	~ ${10}^{-2.4}$
11	~ ${10}^{-2.2}$	~ ${10}^{-2}$

Mei, Z., Cai, K., and Zhong, X. (2019). Neural network-based dynamic threshold detection for non-volatile memories. Proceedings of the IEEE Int. Conf. Commun 20-24 May 2019 Shanghai, China. ICC (pp. 1–6).

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