| 1. |
- Du, G., et al.
(författare)
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Work-in-progress : SSS: Self-aware system-on-chip using static-dynamic hybrid method
- 2017
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Ingår i: Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion, CASES 2017. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450351843
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Konferensbidrag (refereegranskat)abstract
- Network on chip has become the de facto communication standard for multi-core or many-core system on chip, due to its scalability and flexibility. However, temperature is an important factor in NoC design, which affects the overall performance of SoC-decreasing circuit frequency, increasing energy consumption, and even shortening chip lifetime. In this paper, we propose SSS, a self-aware SoC using a static-dynamic hybrid method, which combines dynamic mapping and static mapping to reduce the hot-spots temperature for NoC based SoCs. First, we propose monitoring the thermal distribution for self-state sensoring. Then, in static mapping stage, we calculate the optimal mapping solutions under different temperature modes using discrete firefly algorithm to help self-decision making. Finally, in dynamic mapping stage, we achieve dynamic mapping through configuring NoC and SoC sentient unit for selfoptimizing. Experimental results show SSS can reduce the peak temperature by up to 30.64%. FPGA prototype shows the effectiveness and smartness of SSS in reducing hot-spots temperature. Self-awareness, SoC architecture, NoC.
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| 2. |
- Yao, Yuan, et al.
(författare)
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Work-in-progress : Prediction based convolution neural network acceleration
- 2017
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Ingår i: Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion, CASES 2017. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450351843
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Konferensbidrag (refereegranskat)abstract
- Although intra-layer parallelism is commonly used to expedite CNN execution, it is difficult to achieve inter-layer parallelism because of data dependence between layers. In the paper, we propose a two-phase prediction and correction mechanism to break the data dependence between CNN layers so as to enable inter-layer parallelism. Our technique achieves one more order of magnitude (from the order of 10 to the order of 100) CNN acceleration compared to other three state-of-the-art GPU based CNN acceleration mechanisms.
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