Integrating the principles of approximate computing into the design of
hardware-aware deep neural networks (DNN) has led to DNNs implementations
showing good output quality and highly optimized hardware parameters such as
low latency or inference energy. In this work, we present ApproxDARTS, a neural
architecture search (NAS) method enabling the popular differentiable neural
architecture search method called DARTS to exploit approximate multipliers and
thus reduce the power consumption of generated neural networks. We showed on
the CIFAR-10 data set that the ApproxDARTS is able to perform a complete
architecture search within less than $10$ GPU hours and produce competitive
convolutional neural networks (CNN) containing approximate multipliers in
convolutional layers. For example, ApproxDARTS created a CNN showing an energy
consumption reduction of (a) $53.84\%$ in the arithmetic operations of the
inference phase compared to the CNN utilizing the native $32$-bit
floating-point multipliers and (b) $5.97\%$ compared to the CNN utilizing the
exact $8$-bit fixed-point multipliers, in both cases with a negligible accuracy
drop. Moreover, the ApproxDARTS is $2.3\times$ faster than a similar but
evolutionary algorithm-based method called EvoApproxNAS.

将近似计算的原则融入到硬件感知的深度神经网络（DNN）的设计中，利用名为 DARTS 的可微分神经架构搜索方法，我们提供了一种名为 ApproxDARTS 的神经架构搜索方法，能够利用近似乘法器降低生成的神经网络的能耗。

近似乘法器的可微分神经架构搜索：ApproxDARTS

ApproxDARTS: Differentiable Neural Architecture Search with Approximate  Multipliers

Motivated by the advances in deep learning techniques, the application of
Unmanned Aerial Vehicle (UAV)-based object detection has proliferated across a
range of fields, including vehicle counting, fire detection, and city
monitoring. While most existing research studies only a subset of the
challenges inherent to UAV-based object detection, there are few studies that
balance various aspects to design a practical system for energy consumption
reduction. In response, we present the E3-UAV, an edge-based energy-efficient
object detection system for UAVs. The system is designed to dynamically support
various UAV devices, edge devices, and detection algorithms, with the aim of
minimizing energy consumption by deciding the most energy-efficient flight
parameters (including flight altitude, flight speed, detection algorithm, and
sampling rate) required to fulfill the detection requirements of the task. We
first present an effective evaluation metric for actual tasks and construct a
transparent energy consumption model based on hundreds of actual flight data to
formalize the relationship between energy consumption and flight parameters.
Then we present a lightweight energy-efficient priority decision algorithm
based on a large quantity of actual flight data to assist the system in
deciding flight parameters. Finally, we evaluate the performance of the system,
and our experimental results demonstrate that it can significantly decrease
energy consumption in real-world scenarios. Additionally, we provide four
insights that can assist researchers and engineers in their efforts to study
UAV-based object detection further.

提出了一种基于边缘计算的无人机能效高的目标检测系统，旨在通过决策最节能的飞行参数来降低能源消耗，实验结果表明在实际情景中能够显著降低能源消耗，并提供了对无人机目标检测进一步研究的四个见解。

E3-UAV：无人机边缘能效目标检测系统

E3-UAV: An Edge-based Energy-Efficient Object Detection System for  Unmanned Aerial Vehicles

Fine-tuning models on edge devices like mobile phones would enable
privacy-preserving personalization over sensitive data. However, edge training
has historically been limited to relatively small models with simple
architectures because training is both memory and energy intensive. We present
POET, an algorithm to enable training large neural networks on memory-scarce
battery-operated edge devices. POET jointly optimizes the integrated search
search spaces of rematerialization and paging, two algorithms to reduce the
memory consumption of backpropagation. Given a memory budget and a run-time
constraint, we formulate a mixed-integer linear program (MILP) for
energy-optimal training. Our approach enables training significantly larger
models on embedded devices while reducing energy consumption while not
modifying mathematical correctness of backpropagation. We demonstrate that it
is possible to fine-tune both ResNet-18 and BERT within the memory constraints
of a Cortex-M class embedded device while outperforming current edge training
methods in energy efficiency. POET is an open-source project available at
this https URL

POET 算法可以实现在边缘设备上训练大型神经网络，同时减少能耗并不修改反向传播的数学正确性。在嵌入式设备的内存限制下，使用 POET 可以比当前边缘训练方法更节能地对 ResNet-18 和 BERT 进行微调。