This paper presents a novel end-to-end methodology for enabling the
deployment of low-error deep networks on microcontrollers. To fit the memory
and computational limitations of resource-constrained edge-devices, we exploit
mixed low-bitwidth compression, featuring 8, 4 or 2-bit uniform quantization,
and we model the inference graph with integer-only operations. Our approach
aims at determining the minimum bit precision of every activation and weight
tensor given the memory constraints of a device. This is achieved through a
rule-based iterative procedure, which cuts the number of bits of the most
memory-demanding layers, aiming at meeting the memory constraints. After a
quantization-aware retraining step, the fake-quantized graph is converted into
an inference integer-only model by inserting the Integer Channel-Normalization
(ICN) layers, which introduce a negligible loss as demonstrated on INT4
MobilenetV1 models. We report the latency-accuracy evaluation of
mixed-precision MobilenetV1 family networks on a STM32H7 microcontroller. Our
experimental results demonstrate an end-to-end deployment of an integer-only
Mobilenet network with Top1 accuracy of 68% on a device with only 2MB of FLASH
memory and 512kB of RAM, improving by 8% the Top1 accuracy with respect to
previously published 8 bit implementations for microcontrollers.

本文提出了一种新颖的端到端方法，用于在微控制器上部署低误差的深度神经网络，通过混合低位宽压缩，结合 8、4 或 2 位均匀量化，以整数运算来建模推理图，旨在确定每个激活和权重张量的最小位精度，摆脱了资源受限边缘设备的内存和计算限制，通过一个基于规则的迭代过程，运用量化感知的重训练，将虚假量化图转换为整数推理模型，使用整数通道归一化 (ICN) 图层将该模型部署到只有 2MB 的 FLASH 存储器和 512kB 的 RAM 设备上，并报告了基于 STM32H7 微控制器的混合精度 MobilenetV1 家族网络的延迟 - 精度评估结果，实验结果表明，相比于之前发表的微控制器 8 位实现，Top1 精度提高了 8％，达到了 68％。