  Sound event detection (SED) is a hot topic in consumer and smart city
applications. Existing approaches based on Deep Neural Networks are very
effective, but highly demanding in terms of memory, power, and throughput when
targeting ultra-low power always-on devices.
  Latency, availability, cost, and privacy requirements are pushing recent IoT
systems to process the data on the node, close to the sensor, with a very
limited energy supply, and tight constraints on the memory size and processing
capabilities precluding to run state-of-the-art DNNs.
  In this paper, we explore the combination of extreme quantization to a
small-footprint binary neural network (BNN) with the highly energy-efficient,
RISC-V-based (8+1)-core GAP8 microcontroller. Starting from an existing CNN for
SED whose footprint (815 kB) exceeds the 512 kB of memory available on our
platform, we retrain the network using binary filters and activations to match
these memory constraints. (Fully) binary neural networks come with a natural
drop in accuracy of 12-18% on the challenging ImageNet object recognition
challenge compared to their equivalent full-precision baselines. This BNN
reaches a 77.9% accuracy, just 7% lower than the full-precision version, with
58 kB (7.2 times less) for the weights and 262 kB (2.4 times less) memory in
total. With our BNN implementation, we reach a peak throughput of 4.6 GMAC/s
and 1.5 GMAC/s over the full network, including preprocessing with Mel bins,
which corresponds to an efficiency of 67.1 GMAC/s/W and 31.3 GMAC/s/W,
respectively. Compared to the performance of an ARM Cortex-M4 implementation,
our system has a 10.3 times faster execution time and a 51.1 times higher
energy-efficiency.
