We present an experimental validation of a recently proposed optimization
technique for reservoir computing, using an optoelectronic setup. Reservoir
computing is a robust framework for signal processing applications, and the
development of efficient optimization approaches remains a key challenge. The
technique we address leverages solely a delayed version of the input signal to
identify the optimal operational region of the reservoir, simplifying the
traditionally time-consuming task of hyperparameter tuning. We verify the
effectiveness of this approach on different benchmark tasks and reservoir
operating conditions.

通过使用光电装置验证了一种最近提出的储层计算优化技术的实验。储层计算是一种强大的信号处理应用框架，而高效优化方法的发展仍是一个关键挑战。我们的技术仅利用输入信号的延迟版本来识别储层的最佳操作区域，简化了传统耗时的超参数调整任务。我们验证了这种方法在不同基准任务和储层操作条件下的有效性。

仅使用延迟输入进行物理库计算的高效优化

Efficient Optimisation of Physical Reservoir Computers using only a  Delayed Input

Transpose convolution has shown prominence in many deep learning
applications. However, transpose convolution layers are computationally
intensive due to the increased feature map size due to adding zeros after each
element in each row and column. Thus, convolution operation on the expanded
input feature map leads to poor utilization of hardware resources. The main
reason for unnecessary multiplication operations is zeros at predefined
positions in the input feature map. We propose an algorithmic-level
optimization technique for the effective transpose convolution implementation
to solve these problems. Based on kernel activations, we segregated the
original kernel into four sub-kernels. This scheme could reduce memory
requirements and unnecessary multiplications. Our proposed method was $3.09
(3.02) \times$ faster computation using the Titan X GPU (Intel Dual Core CPU)
with a flower dataset from the Kaggle website. Furthermore, the proposed
optimization method can be generalized to existing devices without additional
hardware requirements. A simple deep learning model containing one transpose
convolution layer was used to evaluate the optimization method. It showed $2.2
\times$ faster training using the MNIST dataset with an Intel Dual-core CPU
than the conventional implementation.

本研究提出了一种基于核激活值的算法级优化技术，将原始内核分成四个子内核，从而降低了内存需求和不必要的乘法运算，提高了转置卷积层的计算效率。实验结果显示，采用所提出的优化方法比传统实现快 $2.2$ 倍。

核分离转置卷积操作

Kernel-Segregated Transpose Convolution Operation

In this paper, we propose a novel indirect monocular SLAM algorithm called
"VITAMIN-E," which is highly accurate and robust as a result of tracking
extremely dense feature points. Typical indirect methods have difficulty in
reconstructing dense geometry because of their careful feature point selection
for accurate matching. Unlike conventional methods, the proposed method
processes an enormous number of feature points by tracking the local extrema of
curvature informed by dominant flow estimation. Because this may lead to high
computational cost during bundle adjustment, we propose a novel optimization
technique, the "subspace Gauss--Newton method", that significantly improves the
computational efficiency of bundle adjustment by partially updating the
variables. We concurrently generate meshes from the reconstructed points and
merge them for an entire 3D model. The experimental results on the SLAM
benchmark dataset EuRoC demonstrated that the proposed method outperformed
state-of-the-art SLAM methods, such as DSO, ORB-SLAM, and LSD-SLAM, both in
terms of accuracy and robustness in trajectory estimation. The proposed method
simultaneously generated significantly detailed 3D geometry from the dense
feature points in real time using only a CPU.

本文提出了一种名为 “VITAMIN-E” 的新型间接单目 SLAM 算法，通过跟踪极其密集的特征点实现高精度和鲁棒性。并且，我们提出了一种名为 “子空间高斯牛顿法” 的优化技术来提高计算效率并整合重建点，从而在 3D 模型重建方面超过了 DSO、ORB-SLAM 和 LSD-SLAM 等最先进的 SLAM 方法的装置。