The principle of equivariance to symmetry transformations enables a
theoretically grounded approach to neural network architecture design.
Equivariant networks have shown excellent performance and data efficiency on
vision and medical imaging problems that exhibit symmetries. Here we show how
this principle can be extended beyond global symmetries to local gauge
transformations. This enables the development of a very general class of
convolutional neural networks on manifolds that depend only on the intrinsic
geometry, and which includes many popular methods from equivariant and
geometric deep learning. We implement gauge equivariant CNNs for signals
defined on the surface of the icosahedron, which provides a reasonable
approximation of the sphere. By choosing to work with this very regular
manifold, we are able to implement the gauge equivariant convolution using a
single conv2d call, making it a highly scalable and practical alternative to
Spherical CNNs. Using this method, we demonstrate substantial improvements over
previous methods on the task of segmenting omnidirectional images and global
climate patterns.

本研究提出了一种基于变换对称性等效性原则的神经网络架构设计方法，并将此原则扩展至局部规范变换领域，以实现在流形上的卷积神经网络，提高了全景图像与全球气候图案分割等任务的性能。

等变规范卷积网络和二十面体 CNN

Gauge Equivariant Convolutional Networks and the Icosahedral CNN

Model quantization is a widely used technique to compress and accelerate deep
neural network (DNN) inference. Emergent DNN hardware accelerators begin to
support mixed precision (1-8 bits) to further improve the computation
efficiency, which raises a great challenge to find the optimal bitwidth for
each layer: it requires domain experts to explore the vast design space trading
off among accuracy, latency, energy, and model size, which is both
time-consuming and sub-optimal. Conventional quantization algorithm ignores the
different hardware architectures and quantizes all the layers in a uniform way.
In this paper, we introduce the Hardware-Aware Automated Quantization (HAQ)
framework which leverages the reinforcement learning to automatically determine
the quantization policy, and we take the hardware accelerator's feedback in the
design loop. Rather than relying on proxy signals such as FLOPs and model size,
we employ a hardware simulator to generate direct feedback signals (latency and
energy) to the RL agent. Compared with conventional methods, our framework is
fully automated and can specialize the quantization policy for different neural
network architectures and hardware architectures. Our framework effectively
reduced the latency by 1.4-1.95x and the energy consumption by 1.9x with
negligible loss of accuracy compared with the fixed bitwidth (8 bits)
quantization. Our framework reveals that the optimal policies on different
hardware architectures (i.e., edge and cloud architectures) under different
resource constraints (i.e., latency, energy and model size) are drastically
different. We interpreted the implication of different quantization policies,
which offer insights for both neural network architecture design and hardware
architecture design.

本文介绍了一种基于深度强化学习的硬件感知自适应量化方法，将硬件加速器的反馈置于设计循环中以自动确定量化策略，该方法可以为不同的神经网络架构和硬件架构专门量身定制量化策略。与传统方法相比，该方法可以将延迟降低 1.4-1.95 倍，能量消耗降低 1.9 倍，并提供了不同量化策略的含义，为神经网络架构设计和硬件架构设计提供了新的思路。