将语音转为脉冲信号，实现低功耗神经形态语音识别
Conjoint Audio-to-Spikes Encoding and Processing for Efficient Neuromorphic Speech Recognition
- 用可编程编码器将音频转为脉冲信号，适配FPGA硬件部署
- 在脉冲编码的海德堡数字数据集上达到99.77%准确率
- 首次端到端评估TIMIT数据集的神经形态编码与分类
从神经形态传感器获取数据并用脉冲神经网络处理，是降低人工智能能耗的有前景方案。当前天然神经形态数据集稀缺，推动了将感官输入转化为脉冲的软件工具发展。然而，高度生物仿真的模拟器在数字硬件上实现存在挑战。本文评估了使用非学习型、高层级、可编程编码器将音频编码为脉冲，并进行后续分类的神经形态方法，目标是在FPGA上实现硬件部署。我们基于定量脉冲活动，采用与硬件无关的指标量化整个流程效率。研究聚焦于编码器与分类器的联合优化：前者提供高效且信息丰富的数据，使后者在训练与推理阶段均以更低能耗获得更优性能。本工作首次实现了对TIMIT数据集的端到端神经形态脉冲编码与评估。简单前馈网络在脉冲编码的海德堡数字数据集上达到99.77%的分类准确率，超越该基准上的神经形态现有水平。
原文摘要 · Abstract (English)
Obtaining data from neuromorphic sensors and processing it with Spiking Neural Networks is a promising solution to lower the energy cost of artificial intelligence. The current rarity of natively neuromorphic datasets promotes the development of software tools to translate input sensory data into spikes. However, highly bio-mimetic simulators can be challenging to implement on digital hardware. In this work, we evaluate the neuromorphic encoding and subsequent classification of audio into spikes using a non-learnable, high-level, programmable encoder targeting hardware implementation on FPGA. We quantify the pipeline's efficiency with hardware-agnostic metrics based on the quantitative spiking activity. Our study focuses on the simultaneous optimisation of encoder and classifier: the first provides efficient and informative data so that the latter achieves a better performance with an overall lower energy cost at learning and inference. This work introduces the first end-to-end neuromorphic spike-encoding and evaluation of the TIMIT dataset. Our simple feedforward network reaches a classification accuracy of 99.77% on a spike-encoded Heidelberg Digits, overcoming the neuromorphic state of the art on this benchmark dataset.
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