aiqbench analysis
This research presents a novel hardware-efficient pipeline that encodes audio directly into spikes for Spiking Neural Networks, achieving state-of-the-art accuracy on benchmark datasets while significantly reducing energy consumption. By optimizing both the encoder and classifier simultaneously, the study addresses the critical challenge of bridging bio-mimetic simulation with practical FPGA implementation for neuromorphic speech recognition.
Conjoint Audio-to-Spikes Encoding and Processing for Efficient Neuromorphic Speech Recognition
arXiv:2608.30792v1 Announce Type: cross Abstract: 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.
Abstract: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.
Under 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
Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
arXiv:2608.30792 [cs.NE]
(or arXiv:2608.30792v1 [cs.NE] for this version)
https://doi.org/10.48550/arXiv.2608.30792 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 273 words
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