推动神经形态计算研究进步的一个关键因素，是能够在重要任务上透明地评估不同的神经形态解决方案，并将其与最先进的传统解决方案进行比较。受 Microsoft DNS Challenge 启发，Intel Neuromorphic Deep Noise Suppression Challenge（Intel N-DNS Challenge，Intel 神经形态深度噪声抑制挑战赛）致力于解决一项无处不在且具有商业价值的任务：实时音频去噪。由于音频去噪具有低带宽和时序特性，且与低功耗设备息息相关，因此极有可能从神经形态计算中获益。Intel N-DNS Challenge 包含两个赛道：一个是基于仿真的算法赛道，旨在鼓励算法创新；另一个是神经形态硬件（Loihi 2）赛道，用于严格评估解决方案。针对这两个赛道，我们制定了一套基于能耗、延迟和资源消耗以及输出音频质量的评估方法。我们免费公开了 Intel N-DNS Challenge 的数据集脚本和评估代码，通过提供奖金鼓励社区参与，并发布了一个神经形态基线解决方案。与 Microsoft NsNet2 和一个用于生产的 Intel 专有去噪模型相比，该基线方案展现出了极具前景的音频质量、高能效和低资源消耗。我们希望 Intel N-DNS Challenge 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。我们期待挑战赛的获胜者能够证明，对于音频去噪这类问题，与当前最先进的传统解决方案相比，在现有的神经形态设备上可以实现功耗和资源方面的显著提升。
英文摘要：
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.
N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家重点实验室）；Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences（中国科学院脑科学与智能技术卓越创新中心）
