The rapid proliferation of the Internet of Things and the dramatic resurgence of artificial
intelligence based application workloads have led to immense interest in performing …

NAND flash storage has become a standard choice in consumer electronics and is gaining
popularity in enterprise systems due to its superior performance and low-power …

Energy-Time reduction trade-offs for error-tolerant applications has called for an attractive
concept known as “Approximate computing”(AC) at software-hardware levels ex-hibiting …

For many applications that exhibit strong error resilience, such as machine learning and
signal processing, energy efficiency and performance can be dramatically improved by …

Quality of real time multimedia images mostly depends on the input, ie size of camera
sensor, pixel density, quality of lens, etc. therefore the output sometimes contains unsolvable …

Smart sensors present in ubiquitous Internet of Things (IoT) devices often obtain high energy
efficiency by carefully tuning how the sensing, the analog to digital (A/D) conversion and the …

Federated learning (FL) has been increasingly widely used for distributed and privacy-
preserving machine learning (ML) environments, as the raw training data can stay local to …

Iterative computing, where the output accuracy gradually improves over multiple iterations,
enables dynamic reconfiguration of energy-quality trade-offs by adjusting the latency (ie …

Abstract Machine learning has proliferated on many Internet-of-Things (IoT) applications
designed for edge devices. Energy efficiency is one of the most crucial constraints in the …

The binary number representation has dominated digital logic for decades due to its
compact storage requirements. However, since the number system is positional, it needs to …