Gate sizing plays an important role in timing optimization after physical
design. Existing machine learning-based gate sizing works cannot optimize
timing on multiple timing paths simultaneously and neglect the physical
constraint on layouts. They cause sub-optimal sizing solutions and
low-efficiency issues when compared with commercial gate sizing tools. In this
work, we propose a learning-driven physically-aware gate sizing framework to
optimize timing performance on large-scale circuits efficiently. In our
gradient descent optimization-based work, for obtaining accurate gradients, a
multi-modal gate sizing-aware timing model is achieved via learning timing
information on multiple timing paths and physical information on
multiple-scaled layouts jointly. Then, gradient generation based on the
sizing-oriented estimator and adaptive back-propagation are developed to update
gate sizes. Our results demonstrate that our work achieves higher timing
performance improvements in a faster way compared with the commercial gate
sizing tool.

我们提出了一种基于学习驱动的物理感知门尺寸调整框架，以有效地在大规模电路上优化时序性能。通过在多个时序路径上学习时序信息和多个尺寸布局上学习物理信息，我们的研究实现了多模态门尺寸感知时序模型，并通过梯度下降优化和自适应反向传播更新门尺寸，取得了与商业门尺寸工具相比更高的时序性能改善和更快的速度。

学习驱动的物理感知大规模电路门尺寸调整

Learning-driven Physically-aware Large-scale Circuit Gate Sizing

Autonomous robotic systems, like autonomous vehicles and robotic search and
rescue, require efficient on-device training for continuous adaptation of Deep
Reinforcement Learning (DRL) models in dynamic environments. This research is
fundamentally motivated by the need to understand and address the challenges of
on-device real-time DRL, which involves balancing timing and algorithm
performance under memory constraints, as exposed through our extensive
empirical studies. This intricate balance requires co-optimizing two pivotal
parameters of DRL training -- batch size and replay buffer size. Configuring
these parameters significantly affects timing and algorithm performance, while
both (unfortunately) require substantial memory allocation to achieve
near-optimal performance.
This paper presents R^3, a holistic solution for managing timing, memory, and
algorithm performance in on-device real-time DRL training. R^3 employs (i) a
deadline-driven feedback loop with dynamic batch sizing for optimizing timing,
(ii) efficient memory management to reduce memory footprint and allow larger
replay buffer sizes, and (iii) a runtime coordinator guided by heuristic
analysis and a runtime profiler for dynamically adjusting memory resource
reservations. These components collaboratively tackle the trade-offs in
on-device DRL training, improving timing and algorithm performance while
minimizing the risk of out-of-memory (OOM) errors.
We implemented and evaluated R^3 extensively across various DRL frameworks
and benchmarks on three hardware platforms commonly adopted by autonomous
robotic systems. Additionally, we integrate R^3 with a popular realistic
autonomous car simulator to demonstrate its real-world applicability.
Evaluation results show that R^3 achieves efficacy across diverse platforms,
ensuring consistent latency performance and timing predictability with minimal
overhead.

本文介绍了 R^3，一种用于管理实时设备上 DRL 训练中的时间、内存和算法性能的整体解决方案。R^3 通过使用动态批处理大小和高效内存管理来优化时间，通过启发式分析和动态调整内存资源预留的运行时协调器，协同解决设备上 DRL 训练中的权衡问题，并实现了在不同平台上实时应用的一致延迟性能和时间可预测性。

R^3：面向自主机器人的设备端实时深度强化学习

R^3: On-device Real-Time Deep Reinforcement Learning for Autonomous  Robotics

Automatic dubbing (AD) is the task of translating the original speech in a
video into target language speech. The new target language speech should
satisfy isochrony; that is, the new speech should be time aligned with the
original video, including mouth movements, pauses, hand gestures, etc. In this
paper, we propose training a model that directly optimizes both the translation
as well as the speech duration of the generated translations. We show that this
system generates speech that better matches the timing of the original speech,
compared to prior work, while simplifying the system architecture.

本研究提出了一种自动配音的模型，通过同时进行翻译和语音持续时间的优化，实现生成的配音更好地匹配原始视频的时间轴，同时简化了整个系统的结构。