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Half-precision Floating Point on Spiking Neural Networks Simulations in FPGA

机译：FPGA中尖峰神经网络仿真的半精度浮点数

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The use of half-precision floating-point numbers (hFP) in simulations of spiking neural networks (SNN) was investigated. The hFP format is used successfully in computer graphics and video games for storage and data transfer. The IEEE 754-2008 standard settles that arithmetic operations must occur at least on single-precision floating-point format (sFP). This means that it is necessary to convert hFP to sFP for arithmetical operations and reconvert the results to hFP before storing it. The influence of successive conversions when simulating SNN is the main concern of this article. Three methods were used to evaluate the impact of hFP on SNNs: (i) F-I curve, (ii) subthreshold regime, and (iii) the time for the next spike. We have tested the leaky integrate-and-fire and the Izhikevich's neuron model; both presented similar results. The data show that SNNs simulated with sFP present equivalent results when compared to the ones simulated with hFP with identical topology. Such results are important because hFP requires half of the memory space, simpler buses, and lower bandwidth for transferring data. We may infer they require lower clock frequency consequently lower power consumption. These are essential factors for real-time simulation of SNN on embedded electronics. The sFP to hFP conversion circuits, and vice versa, may be implemented using few logical blocks in a field-programmable gate arrays (FPGA) with no relevant Iatency. We conclude that data in the hFP format are suitable for SNNs synthesized in FPGAs, even though such implementations require conversion circuits.
机译：研究了半精确浮点数（hFP）在尖峰神经网络（SNN）的仿真中的使用。 hFP格式已成功用于计算机图形和视频游戏中，以进行存储和数据传输。 IEEE 754-2008标准规定，算术运算必须至少在单精度浮点格式（sFP）上发生。这意味着有必要将hFP转换为sFP以进行算术运算，并在存储之前将结果重新转换为hFP。在模拟SNN时，连续转换的影响是本文的主要关注点。三种方法用于评估hFP对SNN的影响：（i）F-I曲线，（ii）亚阈值状态，以及（iii）下一次峰值的时间。我们已经测试了泄漏的“整合并发射”和伊兹维奇的神经元模型。两者都表现出相似的结果。数据表明，与使用具有相同拓扑的hFP模拟的SNN相比，使用sFP模拟的SNN呈现出相同的结果。这样的结果很重要，因为hFP需要一半的存储空间，更简单的总线和更低的带宽来传输数据。我们可以推断出它们需要较低的时钟频率，从而降低了功耗。这些是在嵌入式电子设备上实时仿真SNN的重要因素。 sFP到hFP的转换电路，反之亦然，可以使用现场可编程门阵列（FPGA）中很少的逻辑块来实现，而没有相关的冗余性。我们得出的结论是，即使此类实现需要转换电路，hFP格式的数据也适用于在FPGA中合成的SNN。

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