In an attempt to follow biological information representation and organization principles, the field of neuromorphic engineering is usually approached bottom-up, from the biophysical models to large-scale integration in silico. While ideal as experimentation platforms for cognitive computing and neuroscience, bottom-up neuromorphic processors have yet to demonstrate an efficiency advantage compared to specialized neural network accelerators for real-world problems. Top-down approaches aim at answering this difficulty by (i) starting from the applicative problem and (ii) investigating how to make the associated algorithms hardware-efficient and biologically-plausible. In order to leverage the data sparsity of spike-based neuromorphic retinas for adaptive edge computing and vision applications, we follow a top-down approach and propose SPOON, a 28-nm event-driven CNN (eCNN). It embeds online learning with only 16.8-% power and 11.8-% area overheads with the biologically-plausible direct random target projection (DRTP) algorithm. With an energy per classification of 313nJ at 0.6V and a 0.32-mm$^2$ area for accuracies of 95.3% (on-chip training) and 97.5% (off-chip training) on MNIST, we demonstrate that SPOON reaches the efficiency of conventional machine learning accelerators while embedding on-chip learning and being compatible with event-based sensors, a point that we further emphasize with N-MNIST benchmarking.

中文翻译：

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28-nm 卷积神经形态处理器支持基于 Spike 的 Retinas 进行在线学习

为了遵循生物信息表示和组织原则，神经形态工程领域通常是自下而上的，从生物物理模型到大规模的计算机集成。虽然作为认知计算和神经科学的实验平台是理想的，但自下而上的神经形态处理器与解决现实世界问题的专用神经网络加速器相比，尚未表现出效率优势。自上而下的方法旨在通过（i）从应用问题开始和（ii）研究如何使相关算法硬件高效且生物学上合理来解决这一难题。为了利用基于尖峰的神经形态视网膜的数据稀疏性进行自适应边缘计算和视觉应用，我们采用自上而下的方法并提出 SPOON，一个 28 纳米事件驱动的 CNN (eCNN)。它通过生物学上合理的直接随机目标投影 (DRTP) 算法嵌入了仅具有 16.8% 功率和 11.8% 面积开销的在线学习。在 0.6V 和 0.32-mm$^2$ 区域的每分类能量为 313nJ，MNIST 上的准确度为 95.3%（片上训练）和 97.5%（片外训练），我们证明 SPOON 达到了效率传统机器学习加速器，同时嵌入片上学习并与基于事件的传感器兼容，我们通过 N-MNIST 基准测试进一步强调了这一点。