VLSI Extreme Learning Machine: A Design Space Exploration

Machine learning predictive modelling high-level synthesis design space exploration

A machine learning-based predictive model design space exploration (DSE) method for high-level synthesis (HLS) is presented. The method creates a predictive model for a training set until a given error threshold is reached and then continues with the exploration using the predictive model avoiding time-consuming synthesis and simulations of new configurations. Results show that the authors’ met...

Clustering in extreme learning machine feature space

Extreme learning machine (ELM), used for the “generalized” single-hidden-layer feedforward networks (SLFNs), is a unified learning platform that can use a widespread type of feature mappings. In theory, ELM can approximate any target continuous function and classify any disjoint regions; in application, many experiment results have already demonstrated the good performance of ELM. In view of th...

A Power Aware Cache and Register File Design Space Exploration

Generalized extreme learning machine acting on a metric space

Generalized Extreme Learning Machine (GELM) is a kind of fast and efficient learning algorithm for training Generalized Single-layer hidden Feedforward Networks (GSLFNs) acting on some metric spaces. However, noisy data often produce over-fitting phenomena in practical applications. Therefore, an improved learning algorithm, called Regularized Generalized Extreme Learning Machine (R-GELM), is p...

Extreme Learning Machine: A Review

Feedforward neural networks (FFNN) have been utilised for various research in machine learning and they have gained a significantly wide acceptance. However, it was recently noted that the feedforward neural network has been functioning slower than needed. As a result, it has created critical bottlenecks among its applications. Extreme Learning Machines (ELM) were suggested as alternative learn...