A Globally Arbitrated Memory Tree for Mixed-Time-Criticality Systems

Embedded systems are increasingly based on multi-core platforms to accommodate a growing number of applications, some of which have real-time requirements. Resources, such as off-chip DRAM, are typically shared between the applications using memory interconnects with different arbitration polices to cater to diverse bandwidth and latency requirements. However, traditional centralized interconnects are not scalable as the number of clients increase. Similarly, current distributed interconnects either cannot satisfy the diverse requirements or have decoupled arbitration stages, resulting in larger area, power and worst-case latency. The four main contributions of this article are: 1) a Globally Arbitrated Memory Tree (GAMT) with a distributed architecture that scales well with the number of cores, 2) an RTL-level implementation that can be configured with five arbitration policies (three distinct and two as special cases), 3) the concept of mixed arbitration policies that allows the policy to be selected individually per core, and 4) a worst-case analysis for a mixed arbitration policy that combines TDM and FBSP arbitration.We compare the performance of GAMT with centralized implementations and show that it can run up to four times faster and have over 51 and 37 percent reduction in area and power consumption, respectively, for a given bandwidth

A generic, scalable and globally arbitrated memory tree for shared DRAM access in real-time systems

Predictable arbitration policies, such as Time Division Multiplexing (TDM) and Round-Robin (RR), are used to provide firm real-time guarantees to clients sharing a single memory resource (DRAM) between the multiple memory clients in multi-core real-time systems. Traditional centralized implementations of predictable arbitration policies in a shared memory bus or interconnect are not scalable in terms of the number of clients. On the other hand, existing distributed memory interconnects are either globally arbitrated, which do not offer diverse service according to the heterogeneous client requirements, or locally arbitrated, which suffers from larger area, power and latency overhead. Moreover, selecting the right arbitration policy according to the diverse and dynamic client requirements in reusable platforms requires a generic re-configurable architecture supporting different arbitration policies. The main contributions in this paper are: (1) We propose a novel generic, scalable and globally arbitrated memory tree (GSMT) architecture for distributed implementation of several predictable arbitration policies. (2) We present an RTL-level implementation of Accounting and Priority assignment (APA) logic of GSMT that can be configured with five different arbitration policies typically used for shared memory access in real-time systems. (3) We compare the performance of GSMT with different centralized implementations by synthesizing the designs in a 40 nm process. Our experiments show that with 64 clients GSMT can run up to four times faster than traditional architectures and have over 51% and 37% reduction in area and power consumption, respectively