(Invited) Enabling On-Device Learning with Deep Spiking Neural Networks for Speech Recognition

Soures, Nicholas; Kudithipudi, Dhireesha; Jacobs-Gedrim, Robin; Agarwal, Sapan; Marinella, Matthew

doi:10.1149/08506.0127ecst

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Another important feature for algorithms on embedded platforms is robustness to device noise. To assess the robustness of the deep-LSM, we mimic device noise in a neuromemristive system by adding Gaussian noise on every read and write operation as in Soures et al (2018). As shown in Table 7, the networks performance suffers very little degradation due to the presence of noise.…”

Section: Methodsmentioning

confidence: 99%

Deep Liquid State Machines With Neural Plasticity for Video Activity Recognition

Soures

Kudithipudi

2019

Front. Neurosci.

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Real-world applications such as first-person video activity recognition require intelligent edge devices. However, size, weight, and power constraints of the embedded platforms cannot support resource intensive state-of-the-art algorithms. Machine learning lite algorithms, such as reservoir computing, with shallow 3-layer networks are computationally frugal as only the output layer is trained. By reducing network depth and plasticity, reservoir computing minimizes computational power and complexity, making the algorithms optimal for edge devices. However, as a trade-off for their frugal nature, reservoir computing sacrifices computational power compared to state-of-the-art methods. A good compromise between reservoir computing and fully supervised networks are the proposed deep-LSM networks. The deep-LSM is a deep spiking neural network which captures dynamic information over multiple time-scales with a combination of randomly connected layers and unsupervised layers. The deep-LSM processes the captured dynamic information through an attention modulated readout layer to perform classification. We demonstrate that the deep-LSM achieves an average of 84.78% accuracy on the DogCentric video activity recognition task, beating state-of-the-art. The deep-LSM also shows up to 91.13% memory savings and up to 91.55% reduction in synaptic operations when compared to similar recurrent neural network models. Based on these results we claim that the deep-LSM is capable of overcoming limitations of traditional reservoir computing, while maintaining the low computational cost associated with reservoir computing.

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Section: Methodsmentioning

confidence: 99%

Deep Liquid State Machines With Neural Plasticity for Video Activity Recognition

Soures

Kudithipudi

2019

Front. Neurosci.

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English Speech Recognition Hybrid Algorithm Based on BP Neural Network

Wang¹,

Sun

2023

Lecture Notes on Data Engineering and Communications Technologies

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Virtual Speech System Based on Sensing Technology and Teaching Management in Universities

Niu

2023

Applied Mathematics and Nonlinear Sciences

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In this paper, digital speech is compressed using discrete Fourier transform, discrete cosine transform, and improved discrete cosine transform, and compressed sensing technology is proposed. Based on the compressed sensing technology, the frequency domain parameter compression algorithm and the speech coding and decoding algorithm are designed, and the interactive 3D virtual speech system design is completed through the pre-processing of the speech system, the extraction of speech features and the design of speech control commands. The virtual voice system designed in this paper is introduced in the teaching management mode of colleges and universities, and the main functions of the system include four major sections: notification management, online Q&A, virtual voice system interaction, and teaching resource management. The virtual voice system built using sensing technology is simulated and tested, and the practical application effect of the system is studied through empirical analysis. The experimental results show that the amplitude of the sound recorded by the compression sensor in the voice sensing experiment is more concentrated, the range is concentrated between [-0.025,0.025], and the detected voice is smaller and more effective than the amplitude recorded by the cell phone. Students were mainly satisfied and very satisfied with the four system functions designed in this paper, and in terms of the online Q&A function, only one student expressed great dissatisfaction and the total number of satisfied people was 119, and the students were highly satisfied with the teaching management of the system designed in this study.

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(Invited) Enabling On-Device Learning with Deep Spiking Neural Networks for Speech Recognition

Cited by 3 publications

References 9 publications

Deep Liquid State Machines With Neural Plasticity for Video Activity Recognition

Deep Liquid State Machines With Neural Plasticity for Video Activity Recognition

English Speech Recognition Hybrid Algorithm Based on BP Neural Network

Virtual Speech System Based on Sensing Technology and Teaching Management in Universities

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