School of Electrical and Information Engineering, Tianjin University, China.
School of Electrical and Information Engineering, Tianjin University, China.
Neural Netw. 2023 Aug;165:31-42. doi: 10.1016/j.neunet.2023.05.026. Epub 2023 May 23.
Spike-based perception brings up a new research idea in the field of neuromorphic engineering. A high-performance biologically inspired flexible spiking neural network (SNN) architecture provides a novel method for the exploration of perception mechanisms and the development of neuromorphic computing systems . In this article, we present a biological-inspired spike-based SNN perception digital system that can realize robust perception. The system employs a fully paralleled pipeline scheme to improve the performance and accelerate the processing of feature extraction. An auditory perception system prototype is realized on ten Intel Cyclone field-programmable gate arrays, which can reach the maximum frequency of 107.28 MHz and the maximum throughput of 5364 Mbps. Our design also achieves the power of 5. 148 W/system and energy efficiency of 845.85 μJ. Our auditory perception implementation is also proved to have superior robustness compared with other SNN systems. We use TIMIT digit speech in noise in accuracy testing. Result shows that it achieves up to 85.75% speech recognition accuracy under obvious noise conditions (signal-to-noise ratio of 20 dB) and maintain small accuracy attenuation with the decline of the signal-to-noise ratio. The overall performance of our proposed system outperforms the state-of-the-art perception system on SNN.
基于尖峰的感知为神经形态工程领域带来了新的研究思路。高性能的生物启发式柔性尖峰神经网络 (SNN) 架构为探索感知机制和开发神经形态计算系统提供了一种新方法。在本文中，我们提出了一种基于生物启发的基于尖峰的 SNN 感知数字系统，可以实现稳健的感知。该系统采用全并行流水线方案来提高性能和加速特征提取的处理。在十个英特尔 Cyclone 现场可编程门阵列上实现了一个听觉感知系统原型，可以达到 107.28MHz 的最高频率和 5364Mbps 的最大吞吐量。我们的设计还实现了 5.148W/system 的功率和 845.85μJ 的能效。与其他 SNN 系统相比，我们的听觉感知实现也被证明具有更高的鲁棒性。我们在准确性测试中使用 TIMIT 数字语音噪声来验证。结果表明，在明显的噪声条件下（信噪比为 20dB），它可以达到高达 85.75%的语音识别准确率，并且随着信噪比的下降，准确率衰减很小。我们提出的系统的整体性能优于 SNN 上的最新感知系统。
