The rapid advancement of deep neural networks has significantly improved
various tasks, such as image and speech recognition. However, as the complexity
of these models increases, so does the computational cost and the number of
parameters, making it difficult to deploy them on resource-constrained devices.
This paper proposes a novel memorization-based inference (MBI) that is compute
free and only requires lookups. Specifically, our work capitalizes on the
inference mechanism of the recurrent attention model (RAM), where only a small
window of input domain (glimpse) is processed in a one time step, and the
outputs from multiple glimpses are combined through a hidden vector to
determine the overall classification output of the problem. By leveraging the
low-dimensionality of glimpse, our inference procedure stores key value pairs
comprising of glimpse location, patch vector, etc. in a table. The computations
are obviated during inference by utilizing the table to read out key-value
pairs and performing compute-free inference by memorization. By exploiting
Bayesian optimization and clustering, the necessary lookups are reduced, and
accuracy is improved. We also present in-memory computing circuits to quickly
look up the matching key vector to an input query. Compared to competitive
compute-in-memory (CIM) approaches, MBI improves energy efficiency by almost
2.7 times than multilayer perceptions (MLP)-CIM and by almost 83 times than
ResNet20-CIM for MNIST character recognition.

本文提出了一种基于记忆的推断方法，结合了循环注意模型的推断机制，使用低维度的窗口进行查询和查找，以减少复杂模型的计算压力和参数量，从而提高计算效率和准确性。

深度学习的模型大小无关、不需要计算、基于记忆的推理

Towards Model-Size Agnostic, Compute-Free, Memorization-based Inference  of Deep Learning

Multi-view deep neural network is perhaps the most successful approach in 3D
shape classification. However, the fusion of multi-view features based on max
or average pooling lacks a view selection mechanism, limiting its application
in, e.g., multi-view active object recognition by a robot. This paper presents
VERAM, a recurrent attention model capable of actively selecting a sequence of
views for highly accurate 3D shape classification. VERAM addresses an important
issue commonly found in existing attention-based models, i.e., the unbalanced
training of the subnetworks corresponding to next view estimation and shape
classification. The classification subnetwork is easily overfitted while the
view estimation one is usually poorly trained, leading to a suboptimal
classification performance. This is surmounted by three essential
view-enhancement strategies: 1) enhancing the information flow of gradient
backpropagation for the view estimation subnetwork, 2) devising a highly
informative reward function for the reinforcement training of view estimation
and 3) formulating a novel loss function that explicitly circumvents view
duplication. Taking grayscale image as input and AlexNet as CNN architecture,
VERAM with 9 views achieves instance-level and class-level accuracy of 95:5%
and 95:3% on ModelNet10, 93:7% and 92:1% on ModelNet40, both are the
state-of-the-art performance under the same number of views.

本文提出了一种名为 VERAM 的递归注意模型，能够主动选择一系列视角进行高精度的三维形状分类，通过三个关键增强策略解决了现有注意力模型中不平衡训练的问题，实现了状态下的最先进水平。

VERAM：视图增强的循环注意力模型用于 3D 形状分类

VERAM: View-Enhanced Recurrent Attention Model for 3D Shape  Classification

We introduce a neural network with a recurrent attention model over a
possibly large external memory. The architecture is a form of Memory Network
(Weston et al., 2015) but unlike the model in that work, it is trained
end-to-end, and hence requires significantly less supervision during training,
making it more generally applicable in realistic settings. It can also be seen
as an extension of RNNsearch to the case where multiple computational steps
(hops) are performed per output symbol. The flexibility of the model allows us
to apply it to tasks as diverse as (synthetic) question answering and to
language modeling. For the former our approach is competitive with Memory
Networks, but with less supervision. For the latter, on the Penn TreeBank and
Text8 datasets our approach demonstrates comparable performance to RNNs and
LSTMs. In both cases we show that the key concept of multiple computational
hops yields improved results.

本文介绍了一种具有循环注意力模型的神经网络，该模型可以扩展到可能很大的外部存储器，并被应用于各种任务，如（合成）问答和语言建模。同时，本文展示了多个计算步骤（跳数）的关键概念可以提高性能表现。