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A Tutorial for Key Problems in the Design of Hybrid Hierarchical NoC Architectures with Wireless/RF
Chunhua Xiao,Zhangqin Huang,Da Li 한국산학기술학회 2013 SmartCR Vol.3 No.6
As processing nodes scale up, it is difficult for traditional electronic networks to supply on-chip communication efficiently due to unacceptable latency, plus power and area consumption. Alternative interconnects, such as radio frequency interconnect (RF-I) and optical interconnect, have been explored as interconnection backbones. Hybrid hierarchical architectures with both traditional interconnects and emerging interconnects have been widely adopted to get excellent trade-off between latency and power. The hybrid hierarchical architecture with a wireless/RF-I backbone is more cost-efficient and feasible due to advantages in complementary metal oxide semiconductor compatibility, compared with other alternative interconnects, and has become one of the mainstreams of chip multi-processor systems. However, how to efficiently utilize the wireless/RF-I backbone is a new challenge for designers. Based on analysis of existing typical hybrid hierarchal wireless/RF-I architectures (HHWAs), the key problems in the Design of HHWAs are proposed here, and related potential solutions are provided. In particular, strategies for resource management of wireless/RF-I are explored in detail, and different solutions are discussed. This work is expected to serve as a basis for future HHWA designs.
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Dongfang Zhang,Yunduan Cui,Yao Xiao,Shengxiang Fu,Suk Won Cha,Namwook Kim,Hongyan Mao,Chunhua Zheng 한국정밀공학회 2024 International Journal of Precision Engineering and Vol.11 No.1
With the rapid development of artificial intelligence, deep reinforcement learning (DRL)-based energy management strategies (EMSs) have become an important research direction for hybrid electric vehicles recently, which still face some problems such as fragile convergence characteristics, slower convergence speed, and unsatisfactory optimization effects. In this research, a novel DRL algorithm, i.e. an improved soft actor-critic (ISAC) algorithm is applied to the EMS of a fuel cell hybrid vehicle (FCHV), in which the priority experience replay (PER) and emphasizing recent experience (ERE) methods are adopted to improve the convergence performance of the algorithm and to enhance the FCHV fuel economy. In addition, the fuel cell durability is also considered in the proposed EMS based on a nonlinear fuel cell degradation model while considering the fuel economy. Results indicate that the FCHV fuel consumption of the proposed EMS is decreased by 7.87%, 2.79%, and 2.44% compared to that of the deep deterministic policy gradient (DDPG)-based, the twin delayed deep deterministic policy gradient (TD3)-based, and the SAC-based EMSs respectively while the fuel consumption gap to the dynamic programming-based EMS is narrowed to 2.37% by the proposed EMS. Moreover, the proposed EMS presents the best training performance considering both the convergence speed and stability, and the convergence speed of the proposed EMS is increased by an average of 47.89% compared to that of the other DRL-based EMSs. Furthermore, the fuel cell durability is improved by more than 95% using the proposed EMS compared to that of the EMS without considering the fuel cell degradation.
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