This paper presents a novel FPGA-based neuromorphic cochlea, leveraging the general-purpose spike-coding algorithm, Spiketrum. The focus of this study is on the development and characterization of this cochlea model, which excels in transforming audio vibrations into biologically realistic auditory spike trains. These spike trains are designed to withstand neural fluctuations and spike losses while accurately encapsulating the spatial and precise temporal characteristics of audio, along with the intensity of incoming vibrations. Noteworthy features include the ability to generate real-time spike trains with minimal information loss and the capacity to reconstruct original signals. This fine-tuning capability allows users to optimize spike rates, achieving an optimal balance between output quality and power consumption. Furthermore, the integration of a feedback system into Spiketrum enables selective amplification of specific features while attenuating others, facilitating adaptive power consumption based on application requirements. The hardware implementation supports both spike-based and non-spike-based processors, making it versatile for various computing systems. The cochlea's ability to encode diverse sensory information, extending beyond sound waveforms, positions it as a promising sensory input for current and future spike-based intelligent computing systems, offering compact and real-time spike train generation.

翻译：本文提出了一种基于FPGA的新型神经形态耳蜗，其利用了通用脉冲编码算法Spiketrum。本研究的重点在于该耳蜗模型的开发与特性表征，该模型擅长将音频振动转换为具有生物真实性的听觉脉冲序列。这些脉冲序列旨在承受神经波动和脉冲丢失，同时精确封装音频的空间与精确时间特性以及输入振动的强度。其显著特性包括能够以最小信息损失生成实时脉冲序列，并具备重建原始信号的能力。这种微调能力使用户能够优化脉冲发放率，从而在输出质量与功耗之间实现最佳平衡。此外，在Spiketrum中集成反馈系统能够选择性放大特定特征并衰减其他特征，从而根据应用需求实现自适应功耗。该硬件实现同时支持基于脉冲和非基于脉冲的处理器，使其适用于各种计算系统。该耳蜗能够编码超越声波波形之外的多种感官信息，这使其成为当前及未来基于脉冲的智能计算系统中一种极具前景的感官输入，可提供紧凑且实时的脉冲序列生成。