Java as a specification language for hardware-software systems
ICCAD '97 Proceedings of the 1997 IEEE/ACM international conference on Computer-aided design
Multimedia Execution Hardware Accelerator
Journal of VLSI Signal Processing Systems - Parallel VLSI architectures for image and video processing
The MOLEN Polymorphic Processor
IEEE Transactions on Computers
Proceedings of the 41st annual Design Automation Conference
The DaCapo benchmarks: java benchmarking development and analysis
Proceedings of the 21st annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications
JTRES '06 Proceedings of the 4th international workshop on Java technologies for real-time and embedded systems
Accelerator design for protein sequence HMM search
Proceedings of the 20th annual international conference on Supercomputing
The Molen compiler for reconfigurable processors
ACM Transactions on Embedded Computing Systems (TECS)
A Java processor architecture for embedded real-time systems
Journal of Systems Architecture: the EUROMICRO Journal
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Hybrid architectures combining the strengths of general-purpose processors with application-specific hardware accelerators can lead to a significant performance improvement. Our hybrid architecture uses a Java Virtual Machine as an abstraction layer to hide the complexity of the hardware/software interface between processor and accelerator from the programmer. The data communication between the accelerator and the processor often incurs a significant cost, which sometimes annihilates the original speedup obtained by the accelerator. This article shows how we minimise this communication cost by dynamically chosing an optimal data layout in the Java heap memory which is distributed over both the accelerator and the processor memory. The proposed self-learning memory allocation strategy finds the optimal location for each Java object's data by means of runtime profiling. The communication cost is effectively reduced by up to 86% for the benchmarks in the DaCapo suite (51% on average).