arXiv subjects

Simon J. Bale

Publications and source records attributed to Simon J. Bale.

3 recordsLinked to original sources

arXiv2022

Multi-objective Optimisation of Digital Circuits based on Cell Mapping in an Industrial EDA Flow

Modern electronic design automation (EDA) tools can handle the complexity of state-of-the-art electronic systems by decomposing them into smaller blocks or cells, introducing different levels of abstraction and staged design flows. However, throughout each independent-optimised design step, overhead and inefficiency can accumulate in the resulting overall design. Performing design-specific optimisation from a more global viewpoint requires more time due to the larger search space, but has the potential to provide solutions with improved performance. In this work, a fully-automated, multi-objective (MO) EDA flow is introduced to address this issue. It specifically tunes drive strength mapping, preceding physical implementation, through multi-objective population-based search algorithms. Designs are evaluated with respect to their power, performance and area (PPA). The proposed approach is aimed at digital circuit optimisation at the block-level, where it is capable of expanding the design space and offers a set of trade-off solutions for different case-specific utilisation. We have applied the proposed MOEDA framework to ISCAS-85 and EPFL benchmark circuits using a commercial 65nm standard cell library. The experimental results demonstrate how the MOEDA flow enhances the solutions initially generated by the standard digital flow, and how simultaneously a significant improvement in PPA metrics is achieved.

cs.NE↗

arXiv2021

Multi-objective Digital Design Optimisation via Improved Drive Granularity Standard Cells

To tackle the complexity of state-of-the-art electronic systems, silicon foundries continuously shrink the technology nodes and electronic design automation (EDA) vendors offer hierarchical design flows to decompose systems into smaller blocks. However, such a staged design methodology consists of various levels of abstraction, where margins will be accumulated and result in degradation of the overall design quality. This limits the full use of capabilities of both the process technology and EDA tools. In this work, a study of drive granularity of standard cells is performed and an interpolation method is proposed for drive option expansion within original cell libraries. These aim to investigate how industrial synthesis tools deal with the drive strength selection using different granularity sets. In addition, a fully-automated, multi-objective (MO) EDA digital flow is introduced for power, performance, area (PPA) optimisation based on drive strength refinement. This population-based search method better handles the increased difficulty of cell selection when using larger logic libraries, producing better optimised solutions than standard tool flow in this case. The achieved experimental results demonstrate how the improved drive granularity cells overall enhance the quality of designs and how a significant improvement in trading off PPA is achieved by the MOEDA flow.

cs.OH↗

arXiv2021

Multi-objective Evolutionary Approach for Efficient Kernel Size and Shape for CNN

While state-of-the-art development in CNN topology, such as VGGNet and ResNet, have become increasingly accurate, these networks are computationally expensive involving billions of arithmetic operations and parameters. To improve the classification accuracy, state-of-the-art CNNs usually involve large and complex convolutional layers. However, for certain applications, e.g. Internet of Things (IoT), where such CNNs are to be implemented on resource-constrained platforms, the CNN architectures have to be small and efficient. To deal with this problem, reducing the resource consumption in convolutional layers has become one of the most significant solutions. In this work, a multi-objective optimisation approach is proposed to trade-off between the amount of computation and network accuracy by using Multi-Objective Evolutionary Algorithms (MOEAs). The number of convolution kernels and the size of these kernels are proportional to computational resource consumption of CNNs. Therefore, this paper considers optimising the computational resource consumption by reducing the size and number of kernels in convolutional layers. Additionally, the use of unconventional kernel shapes has been investigated and results show these clearly outperform the commonly used square convolution kernels. The main contributions of this paper are therefore a methodology to significantly reduce computational cost of CNNs, based on unconventional kernel shapes, and provide different trade-offs for specific use cases. The experimental results further demonstrate that the proposed method achieves large improvements in resource consumption with no significant reduction in network performance. Compared with the benchmark CNN, the best trade-off architecture shows a reduction in multiplications of up to 6X and with slight increase in classification accuracy on CIFAR-10 dataset.

cs.PF↗