The fully connected (FC) layer, one of the most fundamental modules in artificial neural networks (ANN), is often considered difficult and inefficient to train due to issues including the risk of overfitting caused by its large amount of parameters. Based on previous work studying ANN from linear spline perspectives, we propose a spline-based approach that eases the difficulty of training FC layers. Given some dataset, we first obtain a continuous piece-wise linear (CPWL) fit through spline methods such as multivariate adaptive regression spline (MARS). Next, we construct an ANN model from the linear spline model and continue to train the ANN model on the dataset using gradient descent optimization algorithms. Our experimental results and theoretical analysis show that our approach reduces the computational cost, accelerates the convergence of FC layers, and significantly increases the interpretability of the resulting model (FC layers) compared with standard ANN training with random parameter initialization followed by gradient descent optimizations.
Building Hybrid B-Spline And Neural Network Operators
Department of Electrical and Computer Engineering, Carnegie Mellon University（电气与计算机工程系，卡内基梅隆大学）；Software Engineering Institute, Carnegie Mellon University（软件工程研究所，卡内基梅隆大学）
Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networks
University of Toronto（多伦多大学）
介绍了一种用于卷积神经网络（CNN）的硬件推理加速器Loom（LM）。在LM中，可以节省的每一位数据精度都转化为成比例的性能增益。具体来说，对于卷积层，LM的执行时间与权重和激活的精度成反比。对于全连接层，LM的性能与权重的精度成反比。LM的目标是面积和带宽受限的片上系统设计，例如那些在移动设备上无法负担存储片上每层所需的多兆字节缓冲区的设计。因此，在给定数据带宽预算的情况下，LM相对于等效的位并行加速器提高了能量效率和性能。对于权重和激活，LM可以利用轮廓导出的每层精度。然而，在运行时，LM进一步以比层粒度小得多的粒度调整激活精度。此外，它可以自然地以比层更小的粒度利用权重精度可变性。平均而言，在几个图像分类CNN中，对于每个周期可以执行相当于128个16 b x 16 b乘法累加操作的配置，LM的性能比最先进的位并行加速器[1]高出4.38倍，而精度没有任何损失，同时能效高出3.54倍。LM可以在执行性能和能源效率的额外改进上权衡准确性，并且与仅针对激活精度的加速器相比是有利的。我们还研究了2位和4位LM变体，发现每周期2位变体是最节能的。
