Recently, the applications of deep neural network (DNN) have been very
prominent in many fields such as computer vision (CV) and natural language
processing (NLP) due to its superior feature extraction performance. However,
the high-dimension parameter model and large-scale mathematical calculation
restrict the execution efficiency, especially for Internet of Things (IoT)
devices. Different from the previous cloud/edge-only pattern that brings huge
pressure for uplink communication and device-only fashion that undertakes
unaffordable calculation strength, we highlight the collaborative computation
between the device and edge for DNN models, which can achieve a good balance
between the communication load and execution accuracy. Specifically, a
systematic on-demand co-inference framework is proposed to exploit the
multi-branch structure, in which the pre-trained Alexnet is right-sized through
\emph{early-exit} and partitioned at an intermediate DNN layer. The integer
quantization is enforced to further compress transmission bits. As a result, we
establish a new Deep Reinforcement Learning (DRL) optimizer-Soft Actor Critic
for discrete (SAC-d), which generates the \emph{exit point}, \emph{partition
point}, and \emph{compressing bits} by soft policy iterations. Based on the
latency and accuracy aware reward design, such an optimizer can well adapt to
the complex environment like dynamic wireless channel and arbitrary CPU
processing, and is capable of supporting the 5G URLLC. Real-world experiment on
Raspberry Pi 4 and PC shows the outperformance of the proposed solution.

提出了一种基于 Deep Neural Network（DNN）模型的 IoT 设备与边缘协同计算框架，通过多分支结构、智能早停、硬件中间分割与整数量化等技术实现了优秀的通信负载和执行精度平衡，结合基于 Soft Actor Critic（SAC-d）的深度强化学习优化算法实现了动态无线通道和任意 CPU 处理下的适应性支持，并在树莓派 4 和 PC 上进行了实验。

基于软 Actor-Critic 的自适应设备 - 边缘联合推理框架

An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic

We introduce PrivPy, a practical privacy-preserving collaborative computation
framework, especially optimized for machine learning tasks. PrivPy provides an
easy-to-use and highly compatible Python programming front-end which supports
high-level array operations and different secure computation engines to allow
for security assumptions and performance trade-offs. With PrivPy, programmers
can write modern machine learning algorithms conveniently and efficiently in
Python. We also design and implement a new efficient computation engine, with
which people can use competing cloud providers to efficiently perform general
arithmetics over real numbers. We demonstrate the usability and scalability of
PrivPy using common machine learning models (e.g. logistic regression and
convolutional neural networks) and real-world datasets (including a
5000-by-1-million matrix).

PrivPy 是一个优化的机器学习隐私保护计算框架，提供易于使用的 Python 编程前端，支持高级数组操作和不同的安全计算引擎，可以方便高效地编写现代机器学习算法。