Less is more: Efficient pruning for reducing AI memory and computational cost
Deep learning and AI systems have made great headway in recent years, especially in their capabilities of automating complex computational tasks such as image recognition, computer vision and natural language processing. Yet, these systems consist of billions of parameters and require great memory usage as well as expensive computational cost.
The sheer volume of 'Big Data' produced today by various sectors is beginning to overwhelm even the extremely efficient computational techniques developed to sift through all that information. But a new computational framework based on random sampling looks set to finally tame Big Data's ever-growing communication, memory and energy costs…
Neural networks are learning algorithms that approximate the solution to a task by training with available data. However, it is usually unclear how exactly they accomplish this. Two young Basel physicists have now derived mathematical expressions that allow one to calculate the optimal solution without training a network. Their results…