摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑-每日新闻-调查人员发布了关于LiQuid状态机的新报告。根据来自俄亥俄州代顿的新闻，由NewsRx记者报道，研究表明：“尖峰神经网络(SNN)硬件由于其处理大小、Weig HT和功率(SWaP)受限环境中的复杂数据的能力而引起了人们的极大兴趣。尤其是记忆器，通过提供具有异常能量和吞吐量效率的模拟域加速，提供了增强SNN算法的潜力。”我们的新闻记者从德艾顿大学的研究中得到一句话:“在目前的SNN体系结构中,液体状态机(LSM)是一个水库计算(RC)的模型,由于其资源利用率低和训练过程简单而脱颖而出。”本文提出了一种基于Memrist OR-的LSM电路设计方法,并利用SPICE设计了实现LSM电路的CI电路,以保证器件的精确精度.此外,我们还探讨了液体连接度调整以促进实时高效的设计过程.为了评估系统的性能,我们在多个数据集上进行了评估,包括MNIST,TI-46语音数字,ACOUS T与现有的LSM加速器相比，我们的结果显示了相当的精度，同时实现了显著的功耗和能量节约。此外，我们的设计在噪声和神经元缺火的情况下表现出了弹性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Li quid State Machines. According to news originating from Dayton, Ohio, by NewsRx correspondents, research stated, "Spiking neural network (SNN) hardware has gain ed significant interest due to its ability to process complex data in size, weig ht, and power (SWaP) constrained environments. Memristors, in particular, offer the potential to enhance SNN algorithms by providing analog domain acceleration with exceptional energy and throughput efficiency." Our news journalists obtained a quote from the research from the University of D ayton, "Among the current SNN architectures, the Liquid State Machine (LSM), a f orm of Reservoir Computing (RC), stands out due to its low resource utilization and straightforward training process. In this paper, we present a custom memrist or-based LSM circuit design with an online learning methodology. The proposed ci rcuit implementing the LSM is designed using SPICE to ensure precise device leve l accuracy. Furthermore, we explore liquid connectivity tuning to facilitate a r eal-time and efficient design process. To assess the performance of our system, we evaluate it on multiple datasets, including MNIST, TI-46 spoken digits, acous tic drone recordings, and musical MIDI files. Our results demonstrate comparable accuracy while achieving significant power and energy savings when compared to existing LSM accelerators. Moreover, our design exhibits resilience in the prese nce of noise and neuron misfires."
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