Background Deep neural networks (DNNs) have been used in many different applications in recent years for pattern recognition and data mining, and are widely considered to be the dominant algorithmic framework in machine learning. However, DNNs are computationally and energetically intensive algorithms that perform billions of floating-point operations...

A substantial part of high energy consumption (> 60%) and large latency (> 90%) of conventional von-Neumann architectures can be attributed to the unavoidable data movement between the processor and main memory (DRAM). This is perhaps the major limiting factor for big data and machine learning applications whose usage is permeating into practically...