Spiking and Event-driven Neuromorphic Mamba Models for Efficient Speech Recognition
Abstract
Deep learning has greatly advanced automatic speech recognition (ASR), enabling widespread deployment on edge devices such as smartphones and smart home systems. However, the computational and energy demands of deep neural networks pose significant challenges for such resource-constrained deployments, introducing latency and limiting real-time interaction. Neuromorphic computing offers a promising solution by introducing activation sparsity through spiking neural networks (SNNs) and event-driven neural networks, converting dense operations into sparse computations. However, a study that evaluates the hardware benefits of different neuromorphic strategies remains lacking for ASR. This paper explores spiking and event-driven neuromorphic neural networks to improve activation sparsity in the state-of-the-art SpeechMamba model for ASR. We introduce an event-driven SpeechMamba with FATReLU activation, achieving over 60% activation sparsity with less than 1% accuracy degradation on LibriSpeech. We also propose a spiking SpeechMamba that attains over 70% sparsity while using 30% fewer parameters than comparable SNNs. Finally, we develop a cycle-accurate event-driven simulator enabling flexible algorithm-hardware co-exploration, which helps us identify computational bottlenecks and yields over 10% additional efficiency improvements.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of spiking and event-driven neuromorphic models for speech recognition, which significantly improve activation sparsity while maintaining performance. This work represents a meaningful step towards efficient ASR implementations on edge devices, addressing critical challenges in computational demands and energy efficiency.
Methodology
The paper proposes a novel event-driven SpeechMamba model utilizing the FATReLU activation function to enhance activation sparsity while minimizing accuracy loss. The methodology is well-structured, incorporating a multi-stage training pipeline that includes pre-training, threshold initialization, and fine-tuning. The introduction of a spiking variant further diversifies the approach, demonstrating a comprehensive exploration of both event-driven and spiking neural networks. The development of a cycle-accurate event-driven simulator is a significant contribution, enabling detailed analysis of algorithm-hardware interactions.
Results & evaluation
The experiments are robust, utilizing the LibriSpeech dataset to benchmark the proposed models against state-of-the-art methods. The evaluation metrics, including Word Error Rate (WER) and activation sparsity, provide a clear understanding of the models' performance. The results indicate a promising balance between sparsity and accuracy, with the event-driven model achieving over 60% activation sparsity with minimal degradation in performance. However, the paper could benefit from more extensive comparisons with additional neuromorphic architectures to validate the findings.
Reproducibility
The paper provides sufficient details regarding the experimental setup, including model training parameters and the use of the SpeechBrain toolkit, which enhances reproducibility. However, the lack of a direct link to the implementation or additional resources for replication could pose challenges for other researchers.
Limitations
One limitation is the reliance on a single dataset (LibriSpeech), which may not fully capture the generalizability of the models across diverse speech recognition tasks. Additionally, while the paper discusses computational bottlenecks, it does not provide a thorough analysis of potential trade-offs between sparsity and accuracy in different contexts.
Broader impact
The proposed models have significant implications for deploying efficient speech recognition systems on resource-constrained devices, such as smartphones and IoT devices. The advancements in neuromorphic computing could pave the way for more sustainable AI solutions, reducing energy consumption and latency in real-time applications. The main contribution of this paper is the introduction of spiking and event-driven neuromorphic models for speech recognition, which significantly improve activation sparsity while maintaining performance. This work represents a meaningful step towards efficient ASR implementations on edge devices, addressing critical challenges in computational demands and energy efficiency.
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