  Motor-Imagery Brain--Machine Interfaces (MI-BMIs)promise direct and
accessible communication between human brains and machines by analyzing brain
activities recorded with Electroencephalography (EEG). Latency, reliability,
and privacy constraints make it unsuitable to offload the computation to the
cloud. Practical use cases demand a wearable, battery-operated device with low
average power consumption for long-term use. Recently, sophisticated
algorithms, in particular deep learning models, have emerged for classifying
EEG signals. While reaching outstanding accuracy, these models often exceed the
limitations of edge devices due to their memory and computational requirements.
In this paper, we demonstrate algorithmic and implementation optimizations for
EEGNET, a compact Convolutional Neural Network (CNN) suitable for many BMI
paradigms. We quantize weights and activations to 8-bit fixed-point with a
negligible accuracy loss of 0.4% on 4-class MI, and present an energy-efficient
hardware-aware implementation on the Mr.Wolf parallel ultra-low power (PULP)
System-on-Chip (SoC) by utilizing its custom RISC-V ISA extensions and 8-core
compute cluster. With our proposed optimization steps, we can obtain an overall
speedup of 64x and a reduction of up to 85% in memory footprint with respect to
a single-core layer-wise baseline implementation. Our implementation takes only
5.82 ms and consumes 0.627 mJ per inference. With 21.0GMAC/s/W, it is 256x more
energy-efficient than an EEGNET implementation on an ARM Cortex-M7
(0.082GMAC/s/W).
