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:神经形态计算研究的关键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, 并将它们与最先进的传统解决方案进行比较。受 Microsoft DNS 挑战的启发,英特尔神经形态深度噪声抑制挑战(Intel N-DNS)解决了一项普遍而商业相关的任务:实时音频去噪。由于音频去噪具有低带宽、时间性质以及对低功耗设备的相关性,因此它可能从神经形态计算中获得好处。英特尔 N-DNS 挑战包括两个赛道:一个基于仿真的算法赛道,以鼓励算法创新,和一个神经形态硬件(Loihi2)赛道,以严格评估解决方案。对于两个赛道,我们在输出音频质量之外指定了一个评估方法,该方法基于能量、延迟和资源消耗。我们免费提供英特尔 N-DNS 挑战数据集脚本和评估代码,鼓励社区参与并提供奖金,并发布一个神经形态基线解决方案,该解决方案在与 Microsoft NsNet2 和一种在生产中使用的专有英特尔去噪模型相比时显示出有前途的音频质量、高功率效率和低资源消耗。我们希望英特尔 N-DNS 挑战能够促进神经形态算法研究的创新,特别是在实时信号处理的训练工具和方法方面。我们预计,挑战的优胜者将证明,在像音频去噪这样的问题上,与传统的最先进解决方案相比,今天可用的神经形态设备上可以实现大幅提高功率和资源的收益。