Name
Abstract | ||
|---|---|---|
We present two contrasting approaches to achieve time predictability in the embedded compute engine, the basic building block of any Internet of Things (IoT) or Cyber-Physical (CPS) system. The traditional approach offers predictability on top of unpredictable processors with numerous optimizations for enhanced performance and programmability at the cost of huge variability in timing. Approaches such as Worst-Case Execution Time (WCET) analysis of software have been struggling to model the complex timing behavior of the underlying processor to provide guarantees. On the other hand, the inevitable slowdown of Moore's Law and the end of Dennard scaling have curtailed the performance and energy scaling of the processors. This stagnation in conjunction with the importance of cognitive computing have motivated widespread adoption of non-von Neumann accelerators and architectures. We argue that these emerging architectures are inherently time-predictable as they depend on software to orchestrate the computation and data movement and are an excellent match for the real-time processing needs.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3316781.3323489 | Proceedings of the 56th Annual Design Automation Conference 2019 |
Field | DocType | ISBN |
Dennard scaling,Predictability,Computer science,Internet of Things,Real-time computing,Software,Execution time,Scaling,Cognitive computing,Computation,Distributed computing | Conference | 978-1-4503-6725-7 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tulika Mitra | 1 | 2714 | 135.99 |