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A closed-loop ASIC design approach based on logical effort theory and artificial neural networks

机译:基于逻辑努力理论和人工神经网络的闭环ASIC设计方法

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Standard cell library is the backbone of modern day application specific integrated circuit (ASIC) design flow provided by electronic design automation (EDA) vendors worldwide. In these libraries, standard cells are generally available in terms of discrete drive strengths with higher drive strength indicating a faster version of the cell belonging to some predefined logic functionality. However, this leads to increased values of area and power consumption in comparison to a lower drive strength standard cell which has slower response time indicating the underlying tradeoff between speed, area and power. A standard cell with discrete drive strength is not always required during the process of logic synthesis and non-availability of standard cells with fractional drive strengths in the aforementioned libraries hugely impacts the overall performance of resulting digital integrated circuits (ICs) and systems [1]. In this paper, a novel technique has been introduced for on-demand generation and inclusion of standard cells in the logic synthesis process leading to availability of a continuous spectrum of standard cells in terms of drive strengths which ultimately provides a platform for closed-loop ASIC design flow. Logical effort (LE) theory has been utilized alongside artificial neural networks (ANNs) in order to implement the proposed methodology. Extensive circuit simulations have been performed using HSPICE in 130 nm/1.2 V CMOS process technology. Preliminary results are encouraging with up to 41.7% and 62.8% reduction in power-delay product (PDP) and power-delay-area product (PDAP) for a 5-stage gate level test circuit. An 8-bit counter realized using the proposed methodology shows up to 29.77% reduction in power dissipation and 37.8% savings in area at varying capacitive loads when compared to conventional logic synthesis technique which employs a library comprising of standard cells with discrete drive strengths. It is noteworthy that the proposed approach is a general technique which can be easily mapped to high complexity circuits and advanced technology nodes. To support this fact, simulation results have also been provided at 90 nm/1 V CMOS process node for the 5-stage test circuit.
机译:标准单元库是全球电子设计自动化(EDA)供应商提供的现代专用集成电路(ASIC)设计流程的骨干。在这些库中,就离散驱动强度而言,通常可以使用标准单元,而更高的驱动强度则表示属于某些预定义逻辑功能的单元版本更快。但是,与较低的驱动强度标准电池相比,这会导致面积和功耗的增加,后者的响应时间较慢,表明速度,面积和功率之间存在潜在的折衷。在逻辑综合过程中,并非总是需要具有离散驱动强度的标准单元,而上述库中具有分数驱动强度的标准单元的不可用性极大地影响了所得数字集成电路(IC)和系统的整体性能[1] 。在本文中,引入了一种新颖的技术,用于在逻辑综合过程中按需生成和包含标准单元,从而在驱动强度方面提供了连续频谱的标准单元,这最终为闭环ASIC提供了平台设计流程。逻辑努力(LE)理论已与人工神经网络(ANN)一起使用,以实现所提出的方法。使用HSPICE在130 nm / 1.2 V CMOS工艺技术中进行了广泛的电路仿真。初步结果令人鼓舞,用于5级栅极电平测试电路的功率延迟乘积(PDP)和功率延迟区域乘积(PDAP)分别降低了41.7%和62.8%。与采用包含具有离散驱动强度的标准单元的库的常规逻辑综合技术相比,使用建议的方法实现的8位计数器在变化的电容负载下,功耗降低了29.77%,面积节省了37.8%。值得注意的是,提出的方法是一种通用技术,可以轻松地映射到高复杂度电路和先进技术节点。为了支持这一事实,还在5级测试电路的90 nm / 1 V CMOS工艺节点上提供了仿真结果。

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