doi: 10.7873/DATE.2015.0853

Hybrid Adaptive Clock Management for FPGA Processor Acceleration

Alexandru Gheolbănoiu1,a, Lucian Petrică1,b and Sorin Cotofană2

1University POLITEHNICA of Bucharest, Romania.

2Delft University of Technology, The Netherlands.


As FPGAs speed, power efficiency, and logic capacity are increasing, so does the number of applications which make use of FPGA processors. However, due to placement and routing constraints, FPGA processors instruction delay balancing is a real challenge, especially when the implementation approaches the FPGA resource capacity. Consequently, even though some instructions can operate at high frequencies, the slow instructions determine the processor clock period, resulting in the underutilisation of the processor potential. However, the fast instructions latent performance may be harnessed through Adaptive Clock Management (ACM), i.e., by dynamically adapting the clock frequency such that each instruction gets sufficient time for correct completion. Up to date, ACM augmented FPGA processors have been proposed based on Clock Multiplexing (CM), but they suffer from long clock switching delays, which could nullify most of the ACM potential performance gain. This paper proposes an effective FPGA tailored clock manipulation approach able to leverage the ACM potential. We first evaluate Clock Stretching (CS), i.e., the temporary clock period augmentation, as a CM alternative in FPGA processor designs and introduce an FPGA specific CS circuit implementation. Subsequently, we evaluate the advantages and drawbacks of the two techniques and propose a Hybrid ACM, which monitors the processor instruction stream and determines the optimal adaptive clocking strategy in order to provide the maximum speedup for the executing program. Given that CS has very low latency at the expense of limited accuracy and dynamic range we rely on it when the program requires frequent clock period changes. Otherwise we utilise CM, which is rather slow but enables the FPGA processor operation at the edge of its hardware capabilities. We evaluate our proposal on a vector processor mapped on a Xilinx Zynq FPGA. Our experiments indicate that on Sum of Squared Differences algorithm, Neural network, and FIR filter execution traces the hybrid ACM provides up to 14% performance increase over the CM based ACM.

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