Abstract:
A dynamically reconfigurable framework manages processing applications in order to meet time-varying constraints to select an optimal hardware architecture. The optimal architecture satisfies time-varying constraints including for example, supplied power, required performance, accuracy levels, available bandwidth, and quality of output such as image reconstruction. The process of determining an optimal solution is defined in terms of multi-objective optimization using Pareto-optimal realizations.
Abstract:
A dynamically reconfigurable framework manages processing applications in order to meet time-varying constraints to select an optimal hardware architecture. The optimal architecture satisfies time-varying constraints including for example, supplied power, required performance, accuracy levels, available bandwidth, and quality of output such as image reconstruction. The process of determining an optimal solution is defined in terms of multi-objective optimization using Pareto-optimal realizations.
Abstract:
A dynamically reconfigurable framework manages processing applications in order to meet time-varying constraints to select an optimal hardware architecture. The optimal architecture satisfies time-varying constraints including for example, supplied power, required performance, accuracy levels, available bandwidth, and quality of output such as image reconstruction. The process of determining an optimal solution is defined in terms of multi-objective optimization using Pareto-optimal realizations.
Abstract:
A dynamically reconfigurable framework manages processing applications in order to meet time-varying constraints to select an optimal hardware architecture. The optimal architecture satisfies time-varying constraints including for example, supplied power, required performance, accuracy levels, available bandwidth, and quality of output such as image reconstruction. The process of determining an optimal solution is defined in terms of multi-objective optimization using Pareto-optimal realizations.