The popularity of mobile vision creates a demand for advanced compact
computational imaging systems, which call for the development of both a
lightweight optical system and an effective image reconstruction model.
Recently, joint design pipelines come to the research forefront, where the two
significant components are simultaneously optimized via data-driven learning to
realize the optimal system design. However, the effectiveness of these designs
largely depends on the initial setup of the optical system, complicated by a
non-convex solution space that impedes reaching a globally optimal solution. In
this work, we present Global Search Optics (GSO) to automatically design
compact computational imaging systems through two parts: (i) Fused Optimization
Method for Automatic Optical Design (OptiFusion), which searches for diverse
initial optical systems under certain design specifications; and (ii) Efficient
Physic-aware Joint Optimization (EPJO), which conducts parallel joint
optimization of initial optical systems and image reconstruction networks with
the consideration of physical constraints, culminating in the selection of the
optimal solution. Extensive experimental results on the design of three-piece
(3P) sphere computational imaging systems illustrate that the GSO serves as a
transformative end-to-end lens design paradigm for superior global optimal
structure searching ability, which provides compact computational imaging
systems with higher imaging quality compared to traditional methods. The source
code will be made publicly available at this https URL

通过全局搜索光学（GSO）方法，我们提出了高级紧凑计算成像系统的自动设计范例，该方法结合了自动光学设计和图像重建网络的并行优化，综合考虑了物理约束条件以及设计规范，实现了全局优化的结构搜索能力，并为传统方法提供了更高的成像质量。

全球搜索光学：自动探索紧凑计算成像系统的最优解决方案

Global Search Optics: Automatically Exploring Optimal Solutions to  Compact Computational Imaging Systems

We present DeepCache, a principled cache design for deep learning inference
in continuous mobile vision. DeepCache benefits model execution efficiency by
exploiting temporal locality in input video streams. It addresses a key
challenge raised by mobile vision: the cache must operate under video scene
variation, while trading off among cacheability, overhead, and loss in model
accuracy. At the input of a model, DeepCache discovers video temporal locality
by exploiting the video's internal structure, for which it borrows proven
heuristics from video compression; into the model, DeepCache propagates regions
of reusable results by exploiting the model's internal structure. Notably,
DeepCache eschews applying video heuristics to model internals which are not
pixels but high-dimensional, difficult-to-interpret data. Our implementation of
DeepCache works with unmodified deep learning models, requires zero developer's
manual effort, and is therefore immediately deployable on off-the-shelf mobile
devices. Our experiments show that DeepCache saves inference execution time by
18% on average and up to 47%. DeepCache reduces system energy consumption by
20% on average.

DeepCache 是一种基于时间局部性的缓存设计，能提高深度学习推理的效率且适用于移动端应用，能平衡缓存能力、资源开销和模型精度损失等问题，并在实验中展现了一定的推理时间和系统能耗的优化。