Microarchitecture simulators are indispensable tools for microarchitecture
designers to validate, estimate, and optimize new hardware that meets specific
design requirements. While the quest for a fast, accurate and detailed
microarchitecture simulation has been ongoing for decades, existing simulators
excel and fall short at different aspects: (i) Although execution-driven
simulation is accurate and detailed, it is extremely slow and requires
expert-level experience to design. (ii) Trace-driven simulation reuses the
execution traces in pursuit of fast simulation but faces accuracy concerns and
fails to achieve significant speedup. (iii) Emerging deep learning (DL)-based
simulations are remarkably fast and have acceptable accuracy but fail to
provide adequate low-level microarchitectural performance metrics crucial for
microarchitectural bottleneck analysis. Additionally, they introduce
substantial overheads from trace regeneration and model re-training when
simulating a new microarchitecture.
Re-thinking the advantages and limitations of the aforementioned simulation
paradigms, this paper introduces TAO that redesigns the DL-based simulation
with three primary contributions: First, we propose a new training dataset
design such that the subsequent simulation only needs functional trace as
inputs, which can be rapidly generated and reused across microarchitectures.
Second, we redesign the input features and the DL model using self-attention to
support predicting various performance metrics. Third, we propose techniques to
train a microarchitecture agnostic embedding layer that enables fast transfer
learning between different microarchitectural configurations and reduces the
re-training overhead of conventional DL-based simulators. Our extensive
evaluation shows {\ours} can reduce the overall training and simulation time by
18.06x over the state-of-the-art DL-based endeavors.

本文介绍了 TAO，它通过重新设计基于深度学习的模拟方法，提出了一种新的训练数据集设计，使用自注意力机制重新设计输入特征和 DL 模型以预测各种性能指标，并提出了训练一个微架构不可知嵌入层的技术，可以在不同的微架构配置之间进行快速迁移学习，并减少传统 DL 模拟器的重新训练开销。经过广泛的评估，我们的方法相比于现有的 DL 模拟器能够减少总体的训练和模拟时间 18.06 倍。

重新思考基于深度学习的微结构仿真

Tao: Re-Thinking DL-based Microarchitecture Simulation

Scaling object taxonomies is one of the important steps toward a robust
real-world deployment of recognition systems. We have faced remarkable progress
in images since the introduction of the LVIS benchmark. To continue this
success in videos, a new video benchmark, TAO, was recently presented. Given
the recent encouraging results from both detection and tracking communities, we
are interested in marrying those two advances and building a strong large
vocabulary video tracker. However, supervisions in LVIS and TAO are inherently
sparse or even missing, posing two new challenges for training the large
vocabulary trackers. First, no tracking supervisions are in LVIS, which leads
to inconsistent learning of detection (with LVIS and TAO) and tracking (only
with TAO). Second, the detection supervisions in TAO are partial, which results
in catastrophic forgetting of absent LVIS categories during video fine-tuning.
To resolve these challenges, we present a simple but effective learning
framework that takes full advantage of all available training data to learn
detection and tracking while not losing any LVIS categories to recognize. With
this new learning scheme, we show that consistent improvements of various large
vocabulary trackers are capable, setting strong baseline results on the
challenging TAO benchmarks.

该论文提出了一个新的学习框架，结合 LVIS 和 TAO 数据集，解决了监督不足的问题，从而实现在视频识别中的检测和追踪，进而在 TAO 基准之上，提升了大型目标追踪器的表现。