Reasoning and Learning-Based Dynamic Codec Reconfiguration for Varying Processing Requirements in Network-on-Chip

Abstract

Crosstalk interferences and high dynamic power consumption in a network-on-chip (NoC) are two increasingly problematic design issues. Using data codecs can reduce the switching activities on wires that cause crosstalk interferences and high dynamic power. However, data codecs have different overheads in terms of area and performance, and varying capabilities in reducing crosstalk and dynamic power. To adapt to the wide range of processing requirements incurred by applications and operating environments, a reasoning and learning (REAL) framework is proposed for a reconfigurable NoC. REAL dynamically investigates the tradeoffs among reliability, dynamic power reduction, performance, and hardware resource usages to configure the reconfigurable NoC with an appropriate data codec at runtime. As a proof of concept, a 3 × 3 reconfigurable NoC was implemented on Xilinx Virtex-4 field-programmable gate array, which required 8.2% lesser number of slices compared with a conventional NoC. Experiments show that at the same overheads of performance and hardware resources the reconfigurable NoC induces a higher probability toward the reduction of crosstalk interferences and dynamic power consumption.