Dynamically-Tunable Dataflow Architectures Based on Markov Queuing Models
نویسندگان
چکیده
Dataflow architectures are fundamental to achieve high performance in data-intensive applications. They must be optimized elaborate input data arriving at an expected rate, which is not always constant. While worst-case designs can significantly increase hardware resources, more optimistic solutions fail sustain execution phases with throughput, leading system congestion or even computational errors. We present architecture monitor and control dataflow that leverage approximate variants trade off accuracy latency of the processes. Our microarchitecture features online prediction based on queuing models estimate response time select proper variant meet target enabling creation dynamically-tunable systems.
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ژورنال
عنوان ژورنال: Electronics
سال: 2022
ISSN: ['2079-9292']
DOI: https://doi.org/10.3390/electronics11040555