Biblio
Approximate Computing aims at trading off computational accuracy against improvements regarding performance, resource utilization and power consumption by making use of the capability of many applications to tolerate a certain loss of quality. A key issue is the dependency of the impact of approximation on the input data as well as user preferences and environmental conditions. In this context, we therefore investigate the concept of self-adaptive image processing that is able to autonomously adapt 2D-convolution filter operators of different accuracy degrees by means of partial reconfiguration on Field-Programmable-Gate-Arrays (FPGAs). Experimental evaluation shows that the dynamic system is able to better exploit a given error tolerance than any static approximation technique due to its responsiveness to changes in input data. Additionally, it provides a user control knob to select the desired output quality via the metric threshold at runtime.
Different applications concurrently running on modern MPSoCs can interfere with each other when they use shared resources. This interference can cause side channels, i.e., sources of unintended information flow between applications. To prevent such side channels, we propose a hybrid mapping methodology that attempts to ensure spatial isolation, i.e., a mutually-exclusive allocation of resources to applications in the MPSoC. At design time and as a first step, we compute compact and connected application mappings (called shapes). In a second step, run-time management uses this information to map multiple spatially segregated shapes to the architecture. We present and evaluate a (fast) heuristic and an (exact) SAT-based mapper, demonstrating the viability of the approach.