Method for classifying images in databases through deep convolutional networks

Since 2006, deep structured learning, or more commonly called deep learning or hierarchical learning, has become a new area of research in machine learning. In recent years, techniques developed from deep learning research have impacted on a wide range of information and particularly image processing studies, within traditional and new fields, including key aspects of machine learning and artificial intelligence. This paper proposes an alternative scheme for training data management in CNNs, consisting of selective-adaptive data sampling. By means of experiments with the CIFAR10 database for image classification

TensorDash: Exploiting Sparsity to Accelerate Deep Neural Network Training and Inference

TensorDash is a hardware level technique for enabling data-parallel MAC units to take advantage of sparsity in their input operand streams. When used to compose a hardware accelerator for deep learning, TensorDash can speedup the training process while also increasing energy efficiency. TensorDash combines a low-cost, sparse input operand interconnect comprising an 8-input multiplexer per multiplier input, with an area-efficient hardware scheduler. While the interconnect allows a very limited set of movements per operand, the scheduler can effectively extract sparsity when it is present in the activations, weights or gradients of neural networks. Over a wide set of models covering various applications, TensorDash accelerates the training process by $1.95{\times}$ while being $1.89\times$ more energy-efficient, $1.6\times$ more energy efficient when taking on-chip and off-chip memory accesses into account. While TensorDash works with any datatype, we demonstrate it with both single-precision floating-point units and bfloat16