Recently deep neural networks have received considerable attention due to
their ability to extract and represent high-level abstractions in data sets.
Deep neural networks such as fully-connected and convolutional neural networks
have shown excellent performance on a wide range of recognition and
classification tasks. However, their hardware implementations currently suffer
from large silicon area and high power consumption due to the their high degree
of complexity. The power/energy consumption of neural networks is dominated by
memory accesses, the majority of which occur in fully-connected networks. In
fact, they contain most of the deep neural network parameters. In this paper,
we propose sparsely-connected networks, by showing that the number of
connections in fully-connected networks can be reduced by up to 90% while
improving the accuracy performance on three popular datasets (MNIST, CIFAR10
and SVHN). We then propose an efficient hardware architecture based on
linear-feedback shift registers to reduce the memory requirements of the
proposed sparsely-connected networks. The proposed architecture can save up to
90% of memory compared to the conventional implementations of fully-connected
neural networks. Moreover, implementation results show up to 84% reduction in
the energy consumption of a single neuron of the proposed sparsely-connected
networks compared to a single neuron of fully-connected neural networks.

本文介绍了一种基于稀疏连接实现深度神经网络的方法，并提出了一种有效的硬件架构，该方法可以减少高度复杂的神经网络的内存需求，并且可以降低能耗高昂的问题。该方法的实现可以在三个流行数据集上提高准确性，同时减少高达 90% 的连接和存储需求以及 84% 的能耗消耗。

稀疏连接神经网络：实现深度神经网络的高效 VLSI 设计

Sparsely-Connected Neural Networks: Towards Efficient VLSI  Implementation of Deep Neural Networks

Counterfactual Regret Minimization (CFR) is an efficient no-regret learning
algorithm for decision problems modeled as extensive games. CFR's regret bounds
depend on the requirement of perfect recall: players always remember
information that was revealed to them and the order in which it was revealed.
In games without perfect recall, however, CFR's guarantees do not apply. In
this paper, we present the first regret bound for CFR when applied to a general
class of games with imperfect recall. In addition, we show that CFR applied to
any abstraction belonging to our general class results in a regret bound not
just for the abstract game, but for the full game as well. We verify our theory
and show how imperfect recall can be used to trade a small increase in regret
for a significant reduction in memory in three domains: die-roll poker, phantom
tic-tac-toe, and Bluff.

本文提出无法完全回忆的游戏中，针对使用 CFR 算法的一般类游戏的第一个遗憾上限及其不适用性，同时证明使用 CFR 在任何抽象类游戏中都适用，且在三种情况下证明不完全回忆可用于交换少量遗憾和显著降低内存。