A critical enabler for progress in neuromorphic computing research is the ability to transparently evaluate different neuromorphic solutions on important tasks and to compare them to state-of-the-art conventional solutions. The Intel Neuromorphic Deep Noise Suppression Challenge (Intel N-DNS Challenge), inspired by the Microsoft DNS Challenge, tackles a ubiquitous and commercially relevant task: real-time audio denoising. Audio denoising is likely to reap the benefits of neuromorphic computing due to its low-bandwidth, temporal nature and its relevance for low-power devices. The Intel N-DNS Challenge consists of two tracks: a simulation-based algorithmic track to encourage algorithmic innovation, and a neuromorphic hardware (Loihi 2) track to rigorously evaluate solutions. For both tracks, we specify an evaluation methodology based on energy, latency, and resource consumption in addition to output audio quality. We make the Intel N-DNS Challenge dataset scripts and evaluation code freely accessible, encourage community participation with monetary prizes, and release a neuromorphic baseline solution which shows promising audio quality, high power efficiency, and low resource consumption when compared to Microsoft NsNet2 and a proprietary Intel denoising model used in production. We hope the Intel N-DNS Challenge will hasten innovation in neuromorphic algorithms research, especially in the area of training tools and methods for real-time signal processing. We expect the winners of the challenge will demonstrate that for problems like audio denoising, significant gains in power and resources can be realized on neuromorphic devices available today compared to conventional state-of-the-art solutions.

翻译：推动神经形态计算研究进步的关键要素之一，是能够透明地评估不同神经形态解决方案在重要任务上的表现，并将其与最先进的传统解决方案进行比较。受微软DNS挑战赛启发，英特尔神经形态深度噪声抑制挑战赛（Intel N-DNS Challenge）聚焦于一项普遍存在且具有商业价值的任务：实时音频降噪。音频降噪因其低带宽、时间序列特性以及对低功耗设备的相关性，有望从神经形态计算中获益。英特尔N-DNS挑战赛包含两个赛道：基于仿真的算法赛道，旨在鼓励算法创新；以及神经形态硬件（Loihi 2）赛道，用于严格评估解决方案。对于两个赛道，除输出音频质量外，我们还基于能耗、延迟和资源消耗指定了评估方法。我们公开了英特尔N-DNS挑战赛的数据集脚本和评估代码，通过现金奖励鼓励社区参与，并发布了神经形态基线解决方案。与微软NsNet2和英特尔生产环境中使用的专有降噪模型相比，该方案展现出良好的音频质量、高能效和低资源消耗。我们希望英特尔N-DNS挑战赛能够加速神经形态算法研究的创新，尤其是在实时信号处理的训练工具和方法领域。我们期待挑战赛的优胜者能够证明：对于音频降噪等任务，与当前最先进的传统解决方案相比，现有神经形态设备可实现显著的功耗与资源效益。