Performance and robustness of bio-inspired digital liquid state machines: A case study of speech recognition.

Yingyezhe Jin, Peng Li

Abstract: This paper presents a systematic performance and robustness study of bio-inspired digital liquid state machines (LSMs) for the purpose of future hardware implementation. Our work focuses not only on the study of the relation between a broad range of network parameters and performance, but also on the impact of process variability and environmental noise on the bio-inspired LSMs from a circuit implementation perspective. In order to shed light on the implementation of LSMs in digital CMOS technologies, we study the trade-offs between hardware overhead (i.e. precision of synaptic weights and membrane voltage and size of the reservoir) and performance. Assisted with theoretical analysis, we leverage the inherent redundancy of the targeted spiking neural networks to achieve both high performance and low hardware cost for the application of speech recognition. In addition, by modeling several types of catastrophic failure and random error, we show that the LSMs are generally robust. Using three subsets of the TI46 speech corpus to benchmark, we elucidate that in terms of isolated word recognition, the analyzed digital LSMs are very promising for future hardware implementation because of their low overhead, good robustness, and high recognition performance.