Spiketrum: An FPGA-based Implementation of a Neuromorphic Cochlea

TL;DR

This paper introduces Spiketrum, an FPGA-based neuromorphic cochlea that converts audio vibrations into realistic spike trains, supporting adaptive, real-time processing with low power consumption for spike-based computing systems.

Contribution

The study develops and characterizes a novel FPGA-based neuromorphic cochlea utilizing the Spiketrum algorithm, enabling real-time, robust, and adaptable auditory spike train generation.

Findings

Supports real-time spike train generation with minimal information loss

Enables adaptive power consumption through feedback mechanisms

Compatible with spike-based and non-spike-based processors

Abstract

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.