記憶體內運算電路在涉及大量平行運算的仿神經計算與實現高能源效率方面顯示出極好的潛力。記憶體內運算特別適用於需要執行大量矩陣向量乘法的卷積神經網路。
在本研究中，考慮到非揮發性記憶體內運算的硬體侷限性，提出了一個實現壓縮卷積神經網路的量化方法－IMCQ。通過考慮非揮發性記憶體內運算的輸入、記憶體單元與感測放大器的限制，更具體地說，對激勵函數的位元數、網路權重的位元數、矩陣向量乘法(MVM)值的位元數與記憶體的字元線數量進行控管，本論文可以在網路推論過程模擬非揮發性記憶體內運算，使訓練後的神經網路能佈署至記憶體內運算電路。此外，本論文更進一步引入基於Concrete分佈的量化方法至IMCQ中的MVM量化器，用來優化在非揮發性記憶體內運算的變異所引起的最小讀取邊界問題，將原先提出的量化方法更適合用於記憶體內運算電路，本研究將此方法稱作IMCAQ。
本研究通過所提出的IMCAQ應用在卷積層權重總和小於1Mb的LeNet與VGG-Net上，分別進行MNIST與CIFAR-10圖像分類來驗證方法的成效。結果表明將權重與激勵函數量化到2-bit、MVM值量化到4-bit、記憶體字元線開啟9條的條件下，與傳統的基於直通估計器的IMCQ相比，在CIFAR-10上的準確度能提高2.92%。而在權重與激勵函數量化到2-bit條件下，MVM值量化到4-bit與全精度的MVM值相比，MNIST和CIFAR-10數據集的準確性僅下降了0.05％和1.31%。實驗結果表明，所提出的方法對於欲製造及設計用於非揮發性記憶體內運算的晶片系統是有效且有用的。

In-memory computing (IMC) exhibits excellent potential for AI accelerator in-volving massive parallel computations and for achieving high energy efficiency. IMC is especially suitable for convolutional neural networks (CNNs), which need to perform large amounts of matrix-vector multiplications (MVMs).
In this work, we propose a quantization method—“IMCQ”, with consideration of the hardware limitations of nonvolatile IMC (nvIMC) to implement compact CNNs. We simulate nvIMC for parallel computation of multilevel MVMs by considering the con-straints of the sense amplifier in nvIMC—more specifically, the need to manage the fol-lowing: the resolution of activations, weights, MVM values and the number of word line in memory cell. Furthermore, we introduce a Concrete distribution based quantiza-tion method to MVM value quantizer in IMCQ, and this method can optimize the small read margin problem caused by variations in nvIMC. We call the advanced meth-od—“IMCAQ”.
We evaluated the performance of proposed quantization methods on the MNIST and CIFAR-10 image classification tasks by LeNet and VGG-Net, respectively. The results showed 2.92% CIFAR-10 accuracy improves compared with traditional straight-through estimator based quantization method under the conditions which are that weights and activations are quantized to 2 bits, MVM values are quantized to 4-bits, and the 9 opened WLs. The results also showed 0.05% MNIST accuracy and 1.31% CIFAR-10 accuracy drop when MVM values are quantized to 4 bits compared with full-precision MVM values. The experimental results indicate that the proposed method is practical and useful for fabricating real chips intended for use in nvIMC platforms.

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