Abstract-MPSoCs with hierarchical communication infrastructures are promising architectures for low power embedded systems. Multiple CPU clusters are coupled using an Network-onChip (NoC). Our CoreVA-MPSoC targets streaming applications in embedded systems, like signal and video processing. In this work we introduce a tightly coupled shared data memory to each CPU cluster, which can be accessed by all CPUs of a cluster and the NoC with low latency. The main focus is the comparison of different memory architectures and their connection to the NoC. We analyze memory architectures with local data memory only, shared data memory only, and a hybrid architecture integrating both. Implementation results are presented for a 28 nm FD-SOI standard cell technology. A CPU cluster with shared memory shows similar area requirements compared to the local memory architecture. We use post place and route simulations for precise analysis of energy consumption on both cluster and NoC level using the different memory architectures. An architecture with shared data memory shows best performance results in combination with a high resource efficiency. On average, the use of shared memory shows a 17.2% higher throughput for a benchmark suite of 10 applications compared to the use of local memory only. the communication infrastructure goes the on-chip memory architecture, which also has a huge impact on performance and energy efficiency. The main focus of this paper is the comparison of different memory architectures and their interaction with the NoC for many core systems. Compared to traditional processor systems, lots of many cores feature a different memory management, which changes the requirements on memory and NoC infrastructure. Traditional processor systems use a memory hierarchy with several (private and shared) on-chip caches, external DRAM, and a unified address space. This allows for easy programming, but results in unpredictable memory access times. Additionally, the cache logic and the coherence handling require a high amount of chip area and power. Therefore, a lot of Many-Core systems omit data caches and use software-managed scratchpad memories instead, which provide a resource-efficient alternative [1]. For performance reasons, the scratchpad memories are tightly attached to each CPU and communication between CPUs is initiated by software. In [2] we showed that area and power consumption of a single CoreVA CPU's data memory increases by 10%, when using a cache instead of scratchpad memory. Due to cache coherence issues it can be expected that these values will even increase for a cache-based many core system. Additionally, software-managed scratchpad memories gives full control of data communication to the programmer or an automatic partitioning tool (cf. Section III-E) and allows for a more accurate performance estimation.The many core architecture considered in this work is our CoreVA-MPSoC, which targets streaming applications in embedded and energy-limited systems. Examples for streaming applications are signal pr...

No abstract

No abstract

No abstract

No abstract