Arbitrary-Precision Convolutional Neural Networks on Low-Power IoT Processors

The deployment of Convolutional Neural Networks (CNNs) on resource-constrained IoT devices calls for accurate model re-sizing and optimization. Among the proposed compression strategies, n-ary fixed-point quantization has proven effective in reducing both computational effort and memory footprint with no (or limited) accuracy loss. However, its use requires custom components and special memory allocation strategies which are not available and burdensome to implement on low-power/low-cost cores. In order to bridge this gap, this work introduces Virtual Quantization (VQ), a hardware-friendly compression method which allows to implement equivalent n ary CNNs on general purpose instruction-set architectures. The proposed VQ framework is validated for the IoT family of ARM MCUs (ARM Cortex-M) and tested with three different real-life applications (i.e. Image Classification, Keyword Spotting, Facial Expression Recognition).