There is a growing interest in enhancing compiler optimizations with ML
models, yet interactions between compilers and ML frameworks remain
challenging. Some optimizations require tightly coupled models and compiler
internals,raising issues with modularity, performance and framework
independence. Practical deployment and transparency for the end-user are also
important concerns. We propose ML-Compiler-Bridge to enable ML model
development within a traditional Python framework while making end-to-end
integration with an optimizing compiler possible and efficient. We evaluate it
on both research and production use cases, for training and inference, over
several optimization problems, multiple compilers and its versions, and gym
infrastructures.

我们提出了 ML-Compiler-Bridge，利用传统的 Python 框架，实现了 ML 模型在优化编译器中的开发和高效的端到端集成。通过在多个优化问题、多个编译器及其版本和 gym 基础设施上进行研究和生产应用的评估，解决了模块化、性能和框架独立性等挑战。

下一步的 700 个 ML 启用的编译器优化

The Next 700 ML-Enabled Compiler Optimizations

Fully-Homomorphic Encryption (FHE) offers powerful capabilities by enabling
secure offloading of both storage and computation, and recent innovations in
schemes and implementations have made it all the more attractive. At the same
time, FHE is notoriously hard to use with a very constrained programming model,
a very unusual performance profile, and many cryptographic constraints.
Existing compilers for FHE either target simpler but less efficient FHE schemes
or only support specific domains where they can rely on expert-provided
high-level runtimes to hide complications.
This paper presents a new FHE language called Encrypted Vector Arithmetic
(EVA), which includes an optimizing compiler that generates correct and secure
FHE programs, while hiding all the complexities of the target FHE scheme.
Bolstered by our optimizing compiler, programmers can develop efficient
general-purpose FHE applications directly in EVA. For example, we have
developed image processing applications using EVA, with a very few lines of
code.
EVA is designed to also work as an intermediate representation that can be a
target for compiling higher-level domain-specific languages. To demonstrate
this, we have re-targeted CHET, an existing domain-specific compiler for neural
network inference, onto EVA. Due to the novel optimizations in EVA, its
programs are on average 5.3x faster than those generated by CHET. We believe
that EVA would enable a wider adoption of FHE by making it easier to develop
FHE applications and domain-specific FHE compilers.

本文提出了一种新的全同态加密语言 Encrypted Vector Arithmetic（EVA），并配备了优化编译器，使开发人员可以直接使用 EVA 开发高效的通用 FHE 应用程序，并且可以作为目标编译高级特定领域的语言。此外，由于 EVA 中的新型优化，其程序的速度比 CHET 生成的程序平均快 5.3 倍，EVA 将使更广泛的人更容易地开发 FHE 应用程序和特定领域的 FHE 编译器。

EVA：一种用于高效同态计算的加密向量算术语言和编译器

EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient  Homomorphic Computation

We present a deep reinforcement learning approach to minimizing the execution
cost of neural network computation graphs in an optimizing compiler. Unlike
earlier learning-based works that require training the optimizer on the same
graph to be optimized, we propose a learning approach that trains an optimizer
offline and then generalizes to previously unseen graphs without further
training. This allows our approach to produce high-quality execution decisions
on real-world TensorFlow graphs in seconds instead of hours. We consider two
optimization tasks for computation graphs: minimizing running time and peak
memory usage. In comparison to an extensive set of baselines, our approach
achieves significant improvements over classical and other learning-based
methods on these two tasks.

本文提出一种深度强化学习方法，以最小化优化器中神经网络计算图的执行成本。该方法通过离线训练优化器，然后推广到以前未见过的图形，以避免在同一图形上进行更多的训练。通过比较广泛的基线，我们的方法在运行时间和峰值内存使用量上比经典和其他基于学习的方法都取得了显着的改进。