用多值交叉阵列实现神经网络推理，提升能效。
Multibit neural inference in a N-ary crossbar architecture
- 设计仿真框架，支持多值交叉阵列直接计算矩阵乘法。
- 在4×4磁隧道结阵列上实现MNIST识别，准确率达93.56%。
- 发现每单元最优状态数可平衡量化误差与精度，适合低功耗场景。
存内计算（IMC）通过在内存交叉阵列中直接执行模拟矩阵-向量乘法（MVM），实现了神经网络推理，相比传统冯·诺依曼架构具有潜在的能效优势。本文提出一个适用于N-ary交叉阵列的仿真框架，以最少的实现假设完成MVM结果获取。利用(4×4)四态磁隧道结（MTJ）阵列，成功完成了异或（XOR）和MNIST分类任务，其中MNIST准确率达到93.56%（软件基准为97.56%）。主成分分析（PCA）降维显著减少了所需操作数，并提升了软件基准性能，仅小幅降低交叉阵列推理准确率。我们识别出权重量化是主要误差来源，并研究了其与系统性非理想性和随机噪声的协同影响。结果表明，单元级随机噪声因阵列平均效应而影响较小，系统性误差更致命。最终，我们确定了每单元最优状态数，可在量化误差与电阻状态分辨率间取得平衡，使总MVM误差最小化。
原文摘要 · Abstract (English)
In-memory computing (IMC) is a paradigm that enables neural network inference by computing analog matrix-vector multiplications (MVM) directly in memory crossbar arrays, with the potential for energy efficiency gains over conventional von Neumann architectures. In this work we present a simulation framework for N-ary crossbar architectures that retrieves MVM results with minimal implementation assumptions. The XOR and MNIST classification tasks were successfully inferred using a simulated crossbar array of (4x4) 4-states magnetic tunnel junctions (MTJ). MNIST accuracy reached 93.56% (vs. 97.56% software baseline). PCA dimensionality reduction was shown to drastically lower the number of required operations and improve the software baseline, for only a modest reduction in crossbar inference accuracy. We identified weight quantization as the primary error source, and studied its impact alongside systematic non-idealities and random noise. We find that cell-specific random noise is less detrimental than systematic errors due to averaging across the array. Finally, we demonstrate an optimal number of states per cell that balances quantization error against resistance state resolution to minimize total MVM error.
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