Explainable Machine Learning Models and Architectures

Explainable Machine Learning Models and Architectures

This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, and the efficient hardware of machine learning applications.

Machine learning and deep learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video requires large data computational operations at the desired data rate and accuracy. Large data requires more use of integrated circuit (IC) area with embedded bulk memories, which further leads to more IC area. Trade-offs between power consumption, delay, and IC area are always a concern for designers and researchers. New hardware architectures and accelerators are needed to explore and experiment with efficient machine-learning models.

Many real-time applications like the processing of biomedical data in healthcare, smart transportation, satellite image analysis, and IoT-enabled systems have significant opportunities for improvements in terms of accuracy, speed, computational powers, and overall power consumption.

This book deals with efficient machine and deep learning models that support high-speed processors with reconfigurable architectures like graphic processing units (GPUs) and field programmable gate arrays (FPGAs), or any hybrid system. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.