This paper introduces a highly flexible, quantized, memory-efficient, and
ultra-lightweight object detection network, called TinyissimoYOLO. It aims to
enable object detection on microcontrollers in the power domain of milliwatts,
with less than 0.5MB memory available for storing convolutional neural network
(CNN) weights. The proposed quantized network architecture with 422k
parameters, enables real-time object detection on embedded microcontrollers,
and it has been evaluated to exploit CNN accelerators. In particular, the
proposed network has been deployed on the MAX78000 microcontroller achieving
high frame-rate of up to 180fps and an ultra-low energy consumption of only
196{\mu}J per inference with an inference efficiency of more than 106
MAC/Cycle. TinyissimoYOLO can be trained for any multi-object detection.
However, considering the small network size, adding object detection classes
will increase the size and memory consumption of the network, thus object
detection with up to 3 classes is demonstrated. Furthermore, the network is
trained using quantization-aware training and deployed with 8-bit quantization
on different microcontrollers, such as STM32H7A3, STM32L4R9, Apollo4b and on
the MAX78000's CNN accelerator. Performance evaluations are presented in this
paper.

TinyissimoYOLO 是一种高度灵活、量化、内存高效、超轻量级的目标检测网络，旨在实现毫瓦级嵌入式微控制器上的实时目标检测；通过量化网络架构实现实时目标检测，最多支持 3 个对象检测类，并可用于不同型号的微控制器。

TinyissimoYOLO: 一种量化、低内存占用、适用于低功耗微控制器的小型机器学习目标检测网络

TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object  Detection Network for Low Power Microcontrollers

Data-free quantization (DFQ) recovers the performance of quantized network
(Q) without accessing the real data, but generates the fake sample via a
generator (G) by learning from full-precision network (P) instead. However,
such sample generation process is totally independent of Q, specialized as
failing to consider the adaptability of the generated samples, i.e., beneficial
or adversarial, over the learning process of Q, resulting into non-ignorable
performance loss. Building on this, several crucial questions -- how to measure
and exploit the sample adaptability to Q under varied bit-width scenarios? how
to generate the samples with desirable adaptability to benefit the quantized
network? -- impel us to revisit DFQ. In this paper, we answer the above
questions from a game-theory perspective to specialize DFQ as a zero-sum game
between two players -- a generator and a quantized network, and further propose
an Adaptability-aware Sample Generation (AdaSG) method. Technically, AdaSG
reformulates DFQ as a dynamic maximization-vs-minimization game process
anchored on the sample adaptability. The maximization process aims to generate
the sample with desirable adaptability, such sample adaptability is further
reduced by the minimization process after calibrating Q for performance
recovery. The Balance Gap is defined to guide the stationarity of the game
process to maximally benefit Q. The theoretical analysis and empirical studies
verify the superiority of AdaSG over the state-of-the-arts. Our code is
available at this https URL

本文通过博弈论的角度，提出了一种适应性样本生成方法，通过重新定义动态最大化与最小化过程，实现了对数据量化过程的优化。