Estimating data bus size for custom processors in embedded systems

Abstract

We propose a method to estimate the data bus width to the requirements of an application that is to run on a custom processor. The proposed estimation method is a simulation-based tool that uses Extreme Value Theory to estimate the width of an off-chip or on-chip data bus based on the characteristics of the application. It finds the minimum number of bus lines needed for the bus connecting the custom processor to other units so that the probability of a multicycle data transfer on the bus is extremely unlikely. The potential target platforms include embedded systems where a custom processor (i.e. an ASIC or a FPGA) in a system-on-a-chip or a system-on-a-board is connected to memory, I/O and other processors through a shared bus or through point-to-point links. Our experimental and analytical results show that our estimation method can reduce the data bus width and cost by up to 66% with an average of 38% for nine benchmarks. The narrower data bus allows us to increase the spacing between the bus lines using the silicon area freed from the eliminated bus lines. This reduces interwire capacitance, which in turn leads to a significant reduction of bus energy consumption. Bus energy can potentially be reduced up to 89% for on-chip data buses with an average of 74% for seven benchmarks. Also, reduction in the interwire capacitance improves the bus propagation delay and on-chip bus propagation delay can be reduced up to 68% with an average of 51% for seven benchmarks using a narrower custom data bus.

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Correspondence to Emre Özer.

Additional information

This study was supported by an Enterprise Ireland Research Innovation Fund Grant IF/2002/035.

Emre Özer is now with ARM Ltd., Cambridge, United Kingdom.

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Özer, E., Nisbet, A.P., Gregg, D. et al. Estimating data bus size for custom processors in embedded systems. Des Autom Embed Syst 10, 5–26 (2005). https://doi.org/10.1007/s10617-006-8706-8

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Keywords

  • Buses
  • Custom processors
  • Embedded systems
  • Extreme value theory
  • Statistics