  Capsule networks (CapsNets) are an emerging trend in image processing. In
contrast to a convolutional neural network, CapsNets are not vulnerable to
object deformation, as the relative spatial information of the objects is
preserved across the network. However, their complexity is mainly related to
the capsule structure and the dynamic routing mechanism, which makes it almost
unreasonable to deploy a CapsNet, in its original form, in a
resource-constrained device powered by a small microcontroller (MCU). In an era
where intelligence is rapidly shifting from the cloud to the edge, this high
complexity imposes serious challenges to the adoption of CapsNets at the very
edge. To tackle this issue, we present an API for the execution of quantized
CapsNets in Arm Cortex-M and RISC-V MCUs. Our software kernels extend the Arm
CMSIS-NN and RISC-V PULP-NN to support capsule operations with 8-bit integers
as operands. Along with it, we propose a framework to perform post-training
quantization of a CapsNet. Results show a reduction in memory footprint of
almost 75%, with accuracy loss ranging from 0.07% to 0.18%. In terms of
throughput, our Arm Cortex-M API enables the execution of primary capsule and
capsule layers with medium-sized kernels in just 119.94 and 90.60 milliseconds
(ms), respectively (STM32H755ZIT6U, Cortex-M7 @ 480 MHz). For the GAP-8 SoC
(RISC-V RV32IMCXpulp @ 170 MHz), the latency drops to 7.02 and 38.03 ms,
respectively.

Machine Learning;Computer Vision and Pattern Recognition