Q EEGNet: an Energy Efficient 8 bit Quantized Parallel EEGNet Implementation for Edge Motor Imagery Brain Machine Interfaces
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 longterm 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 VISA extensions and 8 core compute cluster. With our proposed optimization steps, we can obtain an overall speedup of 64 x 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 21.0 GMAC/s/W, it is 256x more energy efficient than an EEGNET implementation on an ARM Cortex M7 (0.082 GMAC/s/W)
2020 IEEE International Conference on Smart Computing (SMARTCOMP)