Complex-valued neural networks (CVNNs) are nonlinear filters used in the
digital signal processing of complex-domain data. Compared with real-valued
neural networks~(RVNNs), CVNNs can directly handle complex-valued input and
output signals due to their complex domain parameters and activation functions.
With the trend toward low-power systems, computational complexity analysis has
become essential for measuring an algorithm's power consumption. Therefore,
this paper presents both the quantitative and asymptotic computational
complexities of CVNNs. This is a crucial tool in deciding which algorithm to
implement. The mathematical operations are described in terms of the number of
real-valued multiplications, as these are the most demanding operations. To
determine which CVNN can be implemented in a low-power system, quantitative
computational complexities can be used to accurately estimate the number of
floating-point operations. We have also investigated the computational
complexities of CVNNs discussed in some studies presented in the literature.

本文介绍了复值神经网络（CVNNs）的计算复杂度，对浮点运算的数量进行了定量估计，并研究了一些相关研究中所讨论的 CVNNs 的计算复杂度，以便在低功耗系统中实现 CVNNs 算法。

复值神经网络的计算复杂度

On the Computational Complexities of Complex-valued Neural Networks

This paper presents the summary of the Efficient Face Recognition Competition
(EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB
2023). The competition received 17 submissions from 6 different teams. To drive
further development of efficient face recognition models, the submitted
solutions are ranked based on a weighted score of the achieved verification
accuracies on a diverse set of benchmarks, as well as the deployability given
by the number of floating-point operations and model size. The evaluation of
submissions is extended to bias, cross-quality, and large-scale recognition
benchmarks. Overall, the paper gives an overview of the achieved performance
values of the submitted solutions as well as a diverse set of baselines. The
submitted solutions use small, efficient network architectures to reduce the
computational cost, some solutions apply model quantization. An outlook on
possible techniques that are underrepresented in current solutions is given as
well.

2023 年国际联合生物特征识别学术会议上举行了高效人脸识别竞赛（EFaR），本文总结了该竞赛的概况和参赛解决方案的性能表现，以及一个多样性基准集合的解决方案的性能值和基准值。

EFaR 2023: 高效人脸识别竞赛

EFaR 2023: Efficient Face Recognition Competition

To design fast neural networks, many works have been focusing on reducing the
number of floating-point operations (FLOPs). We observe that such reduction in
FLOPs, however, does not necessarily lead to a similar level of reduction in
latency. This mainly stems from inefficiently low floating-point operations per
second (FLOPS). To achieve faster networks, we revisit popular operators and
demonstrate that such low FLOPS is mainly due to frequent memory access of the
operators, especially the depthwise convolution. We hence propose a novel
partial convolution (PConv) that extracts spatial features more efficiently, by
cutting down redundant computation and memory access simultaneously. Building
upon our PConv, we further propose FasterNet, a new family of neural networks,
which attains substantially higher running speed than others on a wide range of
devices, without compromising on accuracy for various vision tasks. For
example, on ImageNet-1k, our tiny FasterNet-T0 is $2.8\times$, $3.3\times$, and
$2.4\times$ faster than MobileViT-XXS on GPU, CPU, and ARM processors,
respectively, while being $2.9\%$ more accurate. Our large FasterNet-L achieves
impressive $83.5\%$ top-1 accuracy, on par with the emerging Swin-B, while
having $36\%$ higher inference throughput on GPU, as well as saving $37\%$
compute time on CPU. Code is available at
https://github.com/JierunChen/FasterNet.

通过提出局部卷积 (PConv) 和一种新的神经网络 FasterNet，实现了在广泛设备上获得比其他网络更快的运行速度，而不会牺牲各种视觉任务的准确性。

追求更高的 FLOPS 以加速神经网络：奔跑不止

Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks

Most of the calculations in standard sphere decoders are redundant, in the
sense that they either calculate quantities that are never used or calculate
some quantities more than once. A new method, which is applicable to lattices
as well as finite constellations, is proposed to avoid these redundant
calculations while still returning the same result. Pseudocode is given to
facilitate immediate implementation. Simulations show that the speed gain with
the proposed method increases linearly with the lattice dimension. At dimension
60, the new algorithms avoid about 75% of all floating-point operations.

本研究提出一种新的方法，避免冗余计算，应用于球形解码器和格点，演示该算法在 60 维时可避免约 75% 的浮点运算。