This work focuses on reducing neural network size, which is a major driver of
neural network execution time, power consumption, bandwidth, and memory
footprint. A key challenge is to reduce size in a manner that can be exploited
readily for efficient training and inference without the need for specialized
hardware. We propose Self-Compression: a simple, general method that
simultaneously achieves two goals: (1) removing redundant weights, and (2)
reducing the number of bits required to represent the remaining weights. This
is achieved using a generalized loss function to minimize overall network size.
In our experiments we demonstrate floating point accuracy with as few as 3% of
the bits and 18% of the weights remaining in the network.

本文介绍了一种称为 Self-Compression 的方法，可以移除神经网络中的冗余参数和减少表示权重所需的位数，从而简化网络结构，提高训练和推断效率。作者通过实验证明，使用 Self-Compression 方法可以在只保留网络中 18% 的权重和仅需 3% 的位数的条件下，还能实现浮点数的准确性。

自压缩神经网络

Self-Compressing Neural Networks

We develop an edge-assisted object recognition system with the aim of
studying the system-level trade-offs between end-to-end latency and object
recognition accuracy. We focus on developing techniques that optimize the
transmission delay of the system and demonstrate the effect of image encoding
rate and neural network size on these two performance metrics. We explore
optimal trade-offs between these metrics by measuring the performance of our
real time object recognition application. Our measurements reveal hitherto
unknown parameter effects and sharp trade-offs, hence paving the road for
optimizing this key service. Finally, we formulate two optimization problems
using our measurement-based models and following a Pareto analysis we find that
careful tuning of the system operation yields at least 33% better performance
for real time conditions, over the standard transmission method.

本文提出了一种基于边缘计算的物体识别系统，研究了端到端延迟和物体识别准确性之间的系统级权衡，重点是优化系统的传输延迟，并证明了图像编码速率和神经网络大小对这两个性能指标的影响；通过测量实时物体识别应用程序的性能，探索最佳权衡点。测量结果显示了迄今未知的参数效应和明显的权衡，从而为优化这一关键服务铺平了道路。最后，使用基于测量的模型制定了两个优化问题，经过 Pareto 分析，发现系统操作的精心调整至少可在实时条件下获得比标准传输方法更高 33% 的性能。