With the substantial increase in the amount of data, the mismatch between the processing speed of the hardware and the software results in the ‘Memory Wall’ problem. Processing-in-memory (PIM), in which the compute and memory units are integrated, can avoid frequent data transmission. Binary neural network (BNN) uses binary weights and activations instead of full-precision weights and activations in the convolutional neural network, which reduces computational complexity with minor influence on accuracy. In this paper, we used a one-step operation to write a pair of SOT-MRAM cells and verified the two basic operations in BNN: XNOR and bitcount. Then, we employed an external control circuit with FPGA and accomplished ‘I’ single-character recognition based on vector-matrix multiplication in the SOT-MRAM array.

中文翻译：

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基于 SOT-MRAM 的二元神经网络单字符识别演示

随着数据量的大幅增加，硬件和软件处理速度的不匹配导致了“内存墙”问题。内存处理（PIM），其中计算和内存单元集成在一起，可以避免频繁的数据传输。二元神经网络 (BNN) 在卷积神经网络中使用二元权重和激活函数，而不是全精度权重和激活函数，这降低了计算复杂度，而对准确性的影响很小。在本文中，我们使用一步操作写入一对 SOT-MRAM 单元，并验证了 BNN 中的两个基本操作：XNOR 和 bitcount。然后，我们采用FPGA的外部控制电路，在SOT-MRAM阵列中实现了基于向量矩阵乘法的'I'单字符识别。