Neural-Network Hardware Drives the Latest Machine-Learning Craze (.PDF Download)
Artificial-intelligence (AI) research covers a number of topics, including machine learning (ML). ML covers a lot of ground as well, from rule-based expert systems to the latest hot trend—neural networks. Neural networks are changing how developers solve problems, whether it be self-driving cars or the industrial Internet of Things (IIoT).
Neural networks come in many forms, but deep neural networks (DNNs) are the most important at this point. A DNN consists of multiple layers, including input and output layers plus multiple hidden layers (Fig. 1). The number of nodes depends on the application; each node has a weight associated with it. An input, such as an image, is supplied at one end and the outputs provide information about the inputs based on the weights.
1. Deep neural networks (right) have multiple hidden layers.
The magnitude of the inputs, weights, and calculations done at each node are of no importance in a logical sense. However, they’re crucial in terms of implementation, because this affects the amount of computational performance necessary for a system, as well as the amount of power required to perform the calculations. The fewer bits involved, the lower the performance and power requirements; hence, a more efficient implementation.