Compact hardware for real-time speech recognition using a Liquid State Machine

B. Schrauwen, Michiel D'Haene, D. Verstraeten, J. V. Campenhout
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引用次数: 23

Abstract

Hardware implementations of Spiking Neural Networks are numerous because they are well suited for implementation in digital and analog hardware, and outperform classic neural networks. This work presents an application driven digital hardware exploration where we implement realtime, isolated digit speech recognition using a Liquid State Machine (a recurrent neural network of spiking neurons where only the output layer is trained). First we test two existing hardware architectures, but they appear to be too fast and thus area consuming for this application. Then we present a scalable, serialised architecture that allows a very compact implementation of spiking neural networks that is still fast enough for real-time processing. This work shows that there is actually a large hardware design space of Spiking Neural Network hardware that can be explored. Existing architectures only spanned part of it.
紧凑的硬件实时语音识别使用的液体状态机
脉冲神经网络的硬件实现有很多,因为它们非常适合在数字和模拟硬件中实现,并且优于经典神经网络。这项工作提出了一种应用驱动的数字硬件探索,我们使用液态机(一种只训练输出层的尖峰神经元的循环神经网络)实现实时、孤立的数字语音识别。首先,我们测试了两种现有的硬件架构,但它们似乎速度太快,因此对这个应用程序来说占用了太多的空间。然后,我们提出了一个可扩展的、序列化的架构,它允许一个非常紧凑的峰值神经网络实现,但仍然足够快,可以进行实时处理。这项工作表明,脉冲神经网络硬件实际上有很大的硬件设计空间可以探索。现有的体系结构只跨越了其中的一部分。
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