SoPC Self-Integration Mechanism for Seamless Architecture Adaptation to Stream Workload Variations

Dumitriu, Victor; Kirischian, Lev

doi:10.1109/tvlsi.2015.2417752

Cited by 9 publications

(15 citation statements)

References 16 publications

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“…This method can be integrated with an RTOS to form a decision-making RTOS that supports run-time system adaptation to a wide range of dynamically varying constraints. Figure 1 shows the general architecture of the MACROS framework [6]. It is formed by: (a) several identical PRRs called "slots" on the FPGA, (b) a Distributed Communication and Control Infrastructure (DCCI), and (c) a Bit-stream and Configuration Management system (BCM).…”

Section: Literature Reviewmentioning

confidence: 99%

“…DCCI, a crossbar switch structure, permits seamless system communication. Inter-communication of task components to find their up-stream and down-stream partners, their self-synchronization and inter-connection also happens over the DCCI [6]. The DCCI thus ensures dynamic and automatic integration of the task components to form the complete ASP circuit of a task.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Step 4-Complete the DPCEM Equation: From (4)- (6) we have: C LS = 0.013, C BS = 1.1, and C DS = 0.226; the coefficients which relate DPC to the number of Logic, BRAM and DSP slices respectively. Also, from Steps 1 to 3, it has been observed that the DPC of each type of resource linearly depends of frequency.…”

Section: Dpcem Derivation Processmentioning

confidence: 99%

“…Also, from Steps 1 to 3, it has been observed that the DPC of each type of resource linearly depends of frequency. A generic equation can thus be formed from (4)- (6), which represents the DPCEM of an FPGA.…”

Section: Dpcem Derivation Processmentioning

confidence: 99%

“…A system must therefore have the infrastructure that allows a quick transformation to the new selected configuration. 'Multi-mode Adaptive Collaborative Reconfigurable self-Organized System' (MACROS) framework has been developed for this purpose [6]. It permits reconfiguration and automatic integration of ASP circuit variants with a very small time overhead in the order of only a couple clock cycles [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Run-Time Mitigation of Power Budget Variations and Hardware Faults by Structural Adaptation of FPGA-Based Multi-Modal SoPC

Sharma

Kirischian

Kirischian³

2018

Computers

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Systems for application domains like robotics, aerospace, defense, autonomous vehicles, etc. are usually developed on System-on-Programmable Chip (SoPC) platforms, capable of supporting several multi-modal computation-intensive tasks on their FPGAs. Since such systems are mostly autonomous and mobile, they have rechargeable power sources and therefore, varying power budgets. They may also develop hardware faults due to radiation, thermal cycling, aging, etc. Systems must be able to sustain the performance requirements of their multi-task multi-modal workload in the presence of variations in available power or occurrence of hardware faults. This paper presents an approach for mitigating power budget variations and hardware faults (transient and permanent) by run-time structural adaptation of the SoPC. The proposed method is based on dynamically allocating, relocating and re-integrating task-specific processing circuits inside the partially reconfigurable FPGA to accommodate the available power budget, satisfy tasks’ performances and hardware resource constraints, and/or to restore task functionality affected by hardware faults. The proposed method has been experimentally implemented on the ARM Cortex-A9 processor of Xilinx Zynq XC7Z020 FPGA. Results have shown that structural adaptation can be done in units of milliseconds since the worst-case decision-making process does not exceed the reconfiguration time of a partial bit-stream.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Dpcem Derivation Processmentioning

confidence: 99%

Section: Dpcem Derivation Processmentioning

confidence: 99%