Approximation for Run-time Power Management

: Anil Kanduri, Mohammad-Hashem Haghbayan, Amir M. Rahmani, Pasi Liljeberg

: No available

: IEEE International Symposium on Circuits and Systems

: 2018

: IEEE International Symposium on Circuits and Systems. Proceedings

: 2018 IEEE International Symposium on Circuits and Systems (ISCAS)

: 2018 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS)

: IEEE INT SYMP CIRC S

: IEEE International Symposium on Circuits and Systems. Proceedings

: 2018

: 4

: 978-1-5386-4882-7

: 0271-4302

: 2379-447X

DOI: https://doi.org/10.1109/ISCAS.2018.8351841

Performance and energy efficiency of multi-core and many-core systems are restricted by increasing power densities and/or limited energy resources. Maximizing performance while minimizing power and energy consumption becomes challenging with emerging workloads. Approximate computing is an alternative solution that offers the required performance and energy gains, leveraging inherent error resilience of specific application domains. Dynamic power management using approximation as another knob can maximize performance and energy efficiency within fixed power budgets. Disciplined tuning of approximation along with other traditional power knobs requires efficient run-time resource management techniques. We present our strategy for using approximation as another knob for tuning the performance loss incurred in power actuation in many-core systems, which is also portable for heterogeneous multi-core systems.