| 1. |
- Chen, Xiaowen, et al.
(författare)
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Reducing Virtual-to-Physical address translation overhead in Distributed Shared Memory based multi-core Network-on-Chips according to data property
- 2013
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Ingår i: Computers & electrical engineering. - : Elsevier BV. - 0045-7906 .- 1879-0755. ; 39:2, s. 596-612
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Tidskriftsartikel (refereegranskat)abstract
- In Network-on-Chip (NoC) based multi-core platforms, Distributed Shared Memory (DSM) preferably uses virtual addressing in order to hide the physical locations of the memories. However, this incurs performance penalty due to the Virtual-to-Physical (V2P) address translation overhead for all memory accesses. Based on the data property which can be either private or shared, this paper proposes a hybrid DSM which partitions a local memory into a private and a shared part. The private part is accessed directly using physical addressing and the shared part using virtual addressing. In particular, the partitioning boundary can be configured statically at design time and dynamically at runtime. The dynamic configuration further removes the V2P address translation overhead for those data with changeable property when they become private at runtime. In the experiments with three applications (matrix multiplication, 2D FFT, and H.264/AVC encoding), compared with the conventional DSM, our techniques show performance improvement up to 37.89%.
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| 2. |
- Chen, Xiaowen, et al.
(författare)
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Multi-bit Transient Fault Control for NoC Links Using 2D Fault Coding Method
- 2016
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Ingår i: 2016 TENTH IEEE/ACM INTERNATIONAL SYMPOSIUM ON NETWORKS-ON-CHIP (NOCS). - : IEEE. - 9781467390309
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Konferensbidrag (refereegranskat)abstract
- In deep nanometer scale, Network-on-Chip (NoC) links are more prone to multi-bit transient fault. Conventional ECC techniques brings heavy area, power, and timing overheads when correcting and detecting multiple transient faults. Therefore, a cost-effective ECC technique, named 2D fault coding method, is adopted to overcome the multi-bit transient fault issue of NoC links. Its key innovation is that the wires of a link are treated as its matrix appearance and light-weight Parity Check Coding (PCC) is performed on the matrix's two dimensions (horizontal matrix rows and vertical matrix columns). Horizontal PCCs and vertical PCCs work together to find the faults' position and then correct them by simply inverting them. The procedure of using the 2D fault coding method to protect a NoC link is proposed, its correction and detection capability is analyzed, and its hardware implementation is carried out. Comparative experiments show that the proposal can largely reduce the ECC hardware cost, have much higher fault detection coverage, maintain almost zero silent fault percentages, and have higher fault correction percentages normalized under the same area, demonstrating that it is cost-effective and suitable to the multi-bit transient fault control for NoC links.
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| 3. |
- Wang, Boqian, et al.
(författare)
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ANN Based Admission Control for On-Chip Networks
- 2019
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Ingår i: PROCEEDINGS OF THE 2019 56TH ACM/EDAC/IEEE DESIGN AUTOMATION CONFERENCE (DAC). - New York, NY, USA : ASSOC COMPUTING MACHINERY.
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Konferensbidrag (refereegranskat)abstract
- We propose an admission control method in Network-on-Chip (NoC) with a centralized Artificial Neural Network (ANN) admission controller, which can improve system performance by predicting the most appropriate injection rate of each node via the network performance information. In the online control process, a data preprocessing unit is applied to simplify the ANN architecture and make the prediction results more accurate. Based on the preprocessed information, the ANN predictor determines the control strategy and broadcasts it to each node where the admission control will be applied. Compared to the previous work, our method builds up a high-fidelity model between the network status and the injection rate regulation. The full-system simulation results show that our proposed method can enhance application performance by 17.8% on average and up to 23.8%.
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