CSR: Small: Scaling the Real-time Capabilities of Powertrain Controllers in Automotive Systems

ScratchPad Memory(SPM)-based many-core architecture is a promising concept to improve the real-time capability of computing systems required in safety-critical applications. Demonstrating the proposed approach for powertrain controllers is just the first step and has the potential to enable the rapidly increasing fields of internet-of-things (IoT), avionics, robotics, cyber-infrastructure, and much more. An open-source cycle-accurate simulator of the SPM-based many-core processor, and a low level virtual machine (llvm) compiler will be developed as the outcome of this research significantly advancing this technology.

This project will develop architecture, compilation, scheduling techniques, and real- time analysis to scale up the real-time capabilities of powertrain controllers of automotive systems. Powertrain controller is a safety-critical, hard-real-time controller in modern automobiles, directly responsible for the operation of the engine and transmission. The complexity of powertrain control applications is rapidly increasing in a quest to fulfill increasingly stringent regulations on the fuel- efficiency and emissions. To meet this demand, the real-time capability of powertrain engine control units (ECU) must also improve. Real-time capability of a processor is essentially a measure of how many/complex/frequent tasks can be guaranteed to finish in given deadlines. The challenge is that the real-time capability of a processor cannot be improved by traditional performance enhancing techniques, e.g., increasing frequency, adding more on-chip memory, or by adding caches. This research will employ (SPMs) instead of caches to improve the real-time capability of powertrain ECUs. SPM partitioning, mapping/scheduling, SPM management techniques and real-time analyses for SPM-based many-core architecture (in which each core has an SPM instead of a cache) will be developed. This will be done for two data management schemes: one for task-level management, where all the code and data of the task are brought into the SPM at the beginning of the task and the other for function-level management, which enables executing applications on processors with much smaller SPM sizes, and can still be efficient through various compiler optimizations.

Agency
National Science Foundation (NSF)
Institute
Division of Computer and Network Systems (CNS)
Type
Standard Grant (Standard)
Application #
1525855
Program Officer
Marilyn McClure
Project Start
Project End
Budget Start
2015-10-01
Budget End
2020-09-30
Support Year
Fiscal Year
2015
Total Cost
$449,506
Indirect Cost
Name
Arizona State University
Department
Type
DUNS #
City
Tempe
State
AZ
Country
United States
Zip Code
85281