Application-aware Retiming of Accelerators: A High-level Data-driven Approach

• Main Authors: Lava, Ana, Mamaghani, Mahdi Jelodari, Mohammadi, Siamak, Furber, Steve
• Format: Journal Article
• Language: English
• Published: 24.12.2016
• Subjects: Computer Science - Distributed, Parallel, and Cluster Computing; Computer Science - Hardware Architecture; Computer Science - Performance
• DOI: 10.48550/arxiv.1612.08163

Flexibility at hardware level is the main driving force behind adaptive systems whose aim is to realise microarhitecture deconfiguration 'online'. This feature allows the software/hardware stack to tolerate drastic changes of the workload in data centres. With emerge of FPGA reconfigurablity this technology is becoming a mainstream computing paradigm. Adaptivity is usually accompanied by the high-level tools to facilitate multi-dimensional space exploration. An essential aspect in this space is memory orchestration where on-chip and off-chip memory distribution significantly influences the architecture in coping with the critical spatial and timing constraints, e.g. Place and Route. This paper proposes a memory smart technique for a particular class of adaptive systems: Elastic Circuits which enjoy slack elasticity at fine level of granularity. We explore retiming of a set of popular benchmarks via investigating the memory distribution within and among accelerators. The area, performance and power patterns are adopted by our high-level synthesis framework, with respect to the behaviour of the input descriptions, to improve the quality of the synthesised elastic circuits.