  We seek to investigate the scalability of neuromorphic computing for computer
vision, with the objective of replicating non-neuromorphic performance on
computer vision tasks while reducing power consumption. We convert the deep
Artificial Neural Network (ANN) architecture U-Net to a Spiking Neural Network
(SNN) architecture using the Nengo framework. Both rate-based and spike-based
models are trained and optimized for benchmarking performance and power, using
a modified version of the ISBI 2D EM Segmentation dataset consisting of
microscope images of cells. We propose a partitioning method to optimize
inter-chip communication to improve speed and energy efficiency when deploying
multi-chip networks on the Loihi neuromorphic chip. We explore the advantages
of regularizing firing rates of Loihi neurons for converting ANN to SNN with
minimum accuracy loss and optimized energy consumption. We propose a percentile
based regularization loss function to limit the spiking rate of the neuron
between a desired range. The SNN is converted directly from the corresponding
ANN, and demonstrates similar semantic segmentation as the ANN using the same
number of neurons and weights. However, the neuromorphic implementation on the
Intel Loihi neuromorphic chip is over 2x more energy-efficient than
conventional hardware (CPU, GPU) when running online (one image at a time).
These power improvements are achieved without sacrificing the task performance
accuracy of the network, and when all weights (Loihi, CPU, and GPU networks)
are quantized to 8 bits.
