Liquid State Machines are brain inspired spiking neural networks (SNNs) with random reservoir connectivity and bio-mimetic neuronal and synaptic models. Reservoir computing networks are proposed as an alternative to deep neural networks to solve temporal classification problems. Previous studies suggest 2nd order (double exponential) synaptic waveform to be crucial for achieving high accuracy for TI-46 spoken digits recognition. The proposal of long-time range (ms) bio-mimetic synaptic waveforms is a challenge to compact and power efficient neuromorphic hardware. In this work, we analyze the role of synaptic orders namely: {\delta} (high output for single time step), 0th (rectangular with a finite pulse width), 1st (exponential fall) and 2nd order (exponential rise and fall) and synaptic timescales on the reservoir output response and on the TI-46 spoken digits classification accuracy under a more comprehensive parameter sweep. We find the optimal operating point to be correlated to an optimal range of spiking activity in the reservoir. Further, the proposed 0th order synapses perform at par with the biologically plausible 2nd order synapses. This is substantial relaxation for circuit designers as synapses are the most abundant components in an in-memory implementation for SNNs. The circuit benefits for both analog and mixed-signal realizations of 0th order synapse are highlighted demonstrating 2-3 orders of savings in area and power consumptions by eliminating Op-Amps and Digital to Analog Converter circuits. This has major implications on a complete neural network implementation with focus on peripheral limitations and algorithmic simplifications to overcome them.

中文翻译：

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液体分类机中用于语音分类的硬件友好型突触顺序和时标

液体状态机是具有随机贮存器连通性和仿生神经元和突触模型的大脑启发性尖峰神经网络（SNN）。提出了储层计算网络作为深度神经网络的替代方案，以解决时间分类问题。先前的研究表明，二阶（双指数）突触波形对于实现TI-46语音数字识别的高精度至关重要。长时间范围（ms）仿生突触波形的建议是对紧凑且功率高效的神经形态硬件的挑战。在这项工作中，我们分析了突触顺序的作用，即{\ delta}（单个时间步的高输出），0th（具有有限脉冲宽度的矩形），在更全面的参数扫描下，油藏输出响应和TI-46语音数字分类精度上的第一（指数下降）和第二阶（指数上升和下降）和突触时标。我们发现最佳工作点与油藏中峰值活动的最佳范围相关。此外，所提出的0阶突触的表现与生物学上合理的2阶突触相当。对于神经网络设计者来说，这是极大的放松，因为突触是SNN的内存实现中最丰富的组件。突出显示了0阶突触的模拟和混合信号实现的电路优势，通过消除运算放大器和数模转换器电路，在面积和功耗方面节省了2-3个数量级。