Mixed-signal implementations of SNNs offer a promising solution to edge computing applications that require low-power and compact embedded processing systems. However, device mismatch in the analog circuits of these neuromorphic processors poses a significant challenge to the deployment of robust processing in these systems. Here we introduce a novel architectural solution inspired by biological development to address this issue. Specifically we propose to implement architectures that incorporate network motifs found in developed brains through a differentiable re-parameterization of weight matrices based on gene expression patterns and genetic rules. Thanks to the gradient descent optimization compatibility of the method proposed, we can apply the robustness of biological neural development to neuromorphic computing. To validate this approach we benchmark it using the Yin-Yang classification dataset, and compare its performance with that of standard multilayer perceptrons trained with state-of-the-art hardware-aware training method. Our results demonstrate that the proposed method mitigates mismatch-induced noise without requiring precise device mismatch measurements, effectively outperforming alternative hardware-aware techniques proposed in the literature, and providing a more general solution for improving the robustness of SNNs in neuromorphic hardware.

翻译：脉冲神经网络（SNN）的混合信号实现为需要低功耗紧凑型嵌入式处理系统的边缘计算应用提供了一种前景广阔的解决方案。然而，这些神经形态处理器中模拟电路的器件失配问题，对这些系统中稳健处理的部署构成了重大挑战。本文提出一种受生物发育启发的新型架构解决方案以应对此问题。具体而言，我们提出通过基于基因表达模式和遗传规则对权重矩阵进行可微分重参数化，来实现包含成熟大脑中发现的网络基序的架构。得益于所提方法的梯度下降优化兼容性，我们可以将生物神经发育的鲁棒性应用于神经形态计算。为验证该方法，我们使用阴阳分类数据集对其进行基准测试，并将其性能与采用最先进的硬件感知训练方法训练的标准多层感知机进行比较。我们的结果表明，所提方法无需精确的器件失配测量即可缓解失配引起的噪声，有效优于文献中提出的其他硬件感知技术，并为提高SNN在神经形态硬件中的鲁棒性提供了更通用的解决方案。