  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.
