  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.
