Recently, both industry and academia have proposed several different neuromorphic
systems to execute machine learning applications that are designed using Spiking Neural …

Multi-core processors support all modern electronic devices nowadays. However, power
management is one of the most critical issues in the design of today's microprocessors. The …

Due to the high integration density and roadblock of voltage scaling, modern multicore
processors experience higher power densities than previous technology scaling nodes …

Location-based services (LBSs) have increasingly provided by a broad range of devices
and applications, but one associated risk is location disclosure. To solve this problem, a …

Q-learning is an off-policy reinforcement learning technique, which has the main advantage
of obtaining an optimal policy interacting with an unknown model environment. This paper …

Artificial intelligence (AI) and Machine Learning (ML) are becoming pervasive in today's
applications, such as autonomous vehicles, healthcare, aerospace, cybersecurity, and many …

Embedded systems execute applications with varying performance requirements. These
applications exercise the hardware differently depending on the computation task …

The pivotal issue of reliability is one of the major concerns for circuit designers. The driving
force is transistor aging, dependent on operating voltage and workload. At the design time, it …

Neuromorphic computing systems such as DYNAPs and Loihi have recently been
introduced to the computing community to improve performance and energy efficiency of …

This paper studies the problem of maximizing multicore system lifetime reliability, an
important design consideration for many real-time embedded systems. Existing work has …