We present an on-chip trainable neuron circuit. Our proposed circuit suits bio-inspired spike-based time-dependent data computation for training spiking neural networks (SNN). The thresholds of neurons can be increased or decreased depending on the desired application-specific spike generation rate. This mechanism provides us with a flexible design and scalable circuit structure. We demonstrate the trainable neuron structure under different operating scenarios. The circuits are designed and optimized for the MIT LL SFQ5ee fabrication process. Margin values for all parameters are above 25\% with a 3GHz throughput for a 16-input neuron.

翻译：我们提出了一种片上可训练神经元电路。该电路适用于基于生物启发脉冲的时间依赖数据计算，用于训练脉冲神经网络（SNN）。神经元的阈值可根据特定应用所需的脉冲生成速率进行增减。这一机制为我们提供了灵活的设计和可扩展的电路结构。我们展示了在不同运行场景下可训练神经元的结构。这些电路针对麻省理工学院林肯实验室SFQ5ee制造工艺进行了设计和优化。所有参数的裕度值均超过25%，16输入神经元的吞吐量达到3GHz。