发表机构
Univ. Bordeaux; Bordeaux INP; CNRS（波尔多大学; 波尔多国立综合理工学院; 法国国家科学研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对神经形态语音识别，开发了面向FPGA的不可学习可编程音频脉冲编码器，优化编码与分类器，在Heidelberg Digits数据集上达99.77%准确率，超越现有技术。
AI 中文摘要
从神经形态传感器获取数据并使用脉冲神经网络（SNN）处理，是降低人工智能能耗的有前景方案。当前原生神经形态数据集稀缺，推动了将输入感官数据转换为脉冲的软件工具开发，但高度仿生的模拟器难以在数字硬件上实现。本研究评估了使用针对FPGA硬件实现的不可学习、高级可编程编码器，将音频编码为脉冲并后续分类的神经形态编码流程，基于脉冲活动的量化指标，采用与硬件无关的指标量化该流程的效率。本研究聚焦于同时优化编码器与分类器：编码器提供高效且信息丰富的数据，使分类器在学习和推理时以更低的总能耗实现更好性能。本研究首次引入端到端神经形态脉冲编码，并在TIMIT数据集上进行评估；采用的简单前馈网络在脉冲编码的Heidelberg Digits数据集上达到99.77%的分类准确率，超越了该基准数据集上的神经形态现有技术水平。
英文摘要
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.
CommentsUnder review. 15 pages, 9 figures and 4 tables. This work is supported by a public grant overseen by the French ANR as part of the "Chaires IA" programme (GrAI project ANR 19 CHIA 0003) and as part of the "PEPR IA France 2030" programme (Emergences project ANR 23 PEIA 0002). This research is part of the programme DesCartes and is supported by the NRF Singapore under its CREATE programme
