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Implementing spiking neural networks on neuromorphic architectures: A review
Recently, both industry and academia have proposed several different neuromorphic
systems to execute machine learning applications that are designed using Spiking Neural …
systems to execute machine learning applications that are designed using Spiking Neural …
A survey of prediction and classification techniques in multicore processor systems
In multicore processor systems, being able to accurately predict the future provides new
optimization opportunities, which otherwise could not be exploited. For example, an oracle …
optimization opportunities, which otherwise could not be exploited. For example, an oracle …
Machine learning for power, energy, and thermal management on multicore processors: A survey
Due to the high integration density and roadblock of voltage scaling, modern multicore
processors experience higher power densities than previous technology scaling nodes …
processors experience higher power densities than previous technology scaling nodes …
A trajectory privacy-preserving scheme based on a dual-K mechanism for continuous location-based services
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 …
and applications, but one associated risk is location disclosure. To solve this problem, a …
Parallel implementation of reinforcement learning Q-learning technique for FPGA
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 …
of obtaining an optimal policy interacting with an unknown model environment. This paper …
Learning transfer-based adaptive energy minimization in embedded systems
Embedded systems execute applications with varying performance requirements. These
applications exercise the hardware differently depending on the computation task …
applications exercise the hardware differently depending on the computation task …
Improving system-level lifetime reliability of multicore soft real-time systems
This paper studies the problem of maximizing multicore system lifetime reliability, an
important design consideration for many real-time embedded systems. Existing work has …
important design consideration for many real-time embedded systems. Existing work has …
Modeling and predicting transistor aging under workload dependency using machine learning
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 …
force is transistor aging, dependent on operating voltage and workload. At the design time, it …
On the role of system software in energy management of neuromorphic computing
Neuromorphic computing systems such as DYNAPs and Loihi have recently been
introduced to the computing community to improve performance and energy efficiency of …
introduced to the computing community to improve performance and energy efficiency of …
[HTML][HTML] Dynamic power management techniques in multi-core architectures: A survey study
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 …
management is one of the most critical issues in the design of today's microprocessors. The …