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Julian Daberkow

dblp:173/1151 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Embedded and real-time systems · 39% Processor architecture and microarchitecture · 30% Memory systems · 30%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Processor architecture and microarchitecture
many-core architecture
0.312018
CoreVA-MPSoC: A Many-Core Architecture with Tightly Coupled Shared and Local Data Memories · IEEE Trans. Parallel Distributed Syst. 2018
Embedded and real-time systems › embedded hardware platform
MPSoC
0.312018
CoreVA-MPSoC: A Many-Core Architecture with Tightly Coupled Shared and Local Data Memories · IEEE Trans. Parallel Distributed Syst. 2018
Memory systems
shared memory
0.312018
CoreVA-MPSoC: A Many-Core Architecture with Tightly Coupled Shared and Local Data Memories · IEEE Trans. Parallel Distributed Syst. 2018
Embedded and real-time systems
energy-efficient embedded systems
0.112018
CoreVA-MPSoC: A Many-Core Architecture with Tightly Coupled Shared and Local Data Memories · IEEE Trans. Parallel Distributed Syst. 2018

Methods — techniques the papers use, named apart from their topics

post place and route simulation · 0.3
YearPublicationVenuePosition
2018 CoreVA-MPSoC: A Many-Core Architecture with Tightly Coupled Shared and Local Data Memories
abstract
MPSoCs with hierarchical communication infrastructures are promising architectures for low power embedded systems. Multiple CPU clusters are coupled using an Network-on-Chip (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 percent higher throughput for a benchmark suite of 10 applications compared to the use of local memory only.
Johannes Ax, Gregor Sievers, Julian Daberkow, Martin Flasskamp, Marten Vohrmann, Thorsten Jungeblut, Wayne Kelly, Mario Porrmann, Ulrich Rückert 0001
IEEE Trans. Parallel Distributed Syst.3