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  1. null (Ed.)
    Electromigration (EM) becomes a major concern for VLSI circuits as the technology advances in the nanometer regime. With Korhonen equations, EM assessment for VLSI circuits remains challenged due to the increasing integrated density. VLSI multisegment interconnect trees can be naturally viewed as graphs. Based on this observation, we propose a new graph convolution network (GCN) model, which is called {\it EMGraph} considering both node and edge embedding features, to estimate the transient EM stress of interconnect trees. Compared with recently proposed generative adversarial network (GAN) based stress image-generation method, EMGraph model can learn more transferable knowledge to predict stress distributions on new graphs without retraining via inductive learning. Trained on the large dataset, the model shows less than 1.5% averaged error compared to the ground truth results and is orders of magnitude faster than both COMSOL and state-of-the-art method. It also achieves smaller model size, 4X accuracy and 14X speedup over the GAN-based method. 
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  2. In this paper, we propose a new dynamic reliability technique using an accuracy-reconfigurable stochastic computing (ARSC) framework for deep learning computing. Unlike the conventional stochastic computing that conducts design time accuracy power/energy trade-off, the new ARSC design can adjust the bit-width of the data in run time. Hence, the ARSC can mitigate the long-term aging effects by slowing the system clock frequency, while maintaining the inference throughput by reducing the data bit-width at a small cost of accuracy. We show how to implement the recently proposed counter-based SC multiplication and bit-width reduction on a layer-wise quantization scheme for CNN networks with dynamic fixed-point data. We validate an ARSC-based five-layer convolutional neural network designs for the MNIST dataset based on Vivado HLS with constraints from Xilinx Zynq-7000 family xc7z045 platform. Experimental results show that new ARSC DNN can sufficiently compensate the NBTI induced aging effects in 10 years with marginal classification accuracy loss while maintaining or even exceeding the pre-aging computing throughput. At the same time, the proposed ARSC computing framework also reduces the active power consumption due to the frequency scaling, which can further improve system reliability due to the reduced temperature. 
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  3. Electromigration (EM) is a major failure effect for on-chip power grid networks of deep submicron VLSI circuits. EM degradation of metal grid lines can lead to excessive voltage drops (IR drops) before the target lifetime. In this paper, we propose a fast data-driven EM-induced IR drop analysis framework for power grid networks, named {\it GridNet}, based on the conditional generative adversarial networks (CGAN). It aims to accelerate the incremental full-chip EM-induced IR drop analysis, as well as IR drop violation fixing during the power grid design and optimization. More importantly, {\it GridNet} can naturally leverage the differentiable feature of deep neural networks (DNN) to {\it obtain the sensitivity information of node voltage with respect to the wire resistance (or width) with marginal cost}. {\it GridNet} treats continuous time and the given electrical features as input conditions, and the EM-induced time-varying voltage of power grid networks as the conditional outputs, which are represented as data series images. We show that {\it GridNet} is able to learn the temporal dynamics of the aging process in continuous time domain. Besides, we can take advantage of the sensitivity information provided by {\it GridNet} to perform efficient localized IR drop violation fixing in the late stage design and optimization. Numerical results on 36000 synthesized power grid network samples demonstrate that the new method can lead to $10^5\times$ speedup over the recently proposed full-chip coupled EM and IR drop analysis tool. We further show that localized IR drop violation fix for the same set of power grid networks can be performed remarkably efficiently using the cheap sensitivity computation from {\it GridNet}. 
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  4. Electromigration (EM) is still the most important reliability concern for VLSI systems, especially at the nanometer regime. EM immortality check is an important step for full-chip EM signoff analysis. In this paper, we propose a new electromigration (EM) immortality check method for multi-segment interconnect considering the impacts of Joule heating induced temperature gradient. Temperature gradients from metal Joule heating, called thermal migration, can be a significant force for the metal atomic migrations, and these impacts get more significant as technology scales down. Compared to existing methods, the new method can consider the spatial temperature gradient due to Joule heating for multi-segment wires for the first time. We derive the analytic solution for the resulting steady-state EM-thermal migration stress distribution problem. Then we develop the new temperature-aware voltage-based EM immortality check method considering the multi-segment temperature migration effects, which carries all the benefits of the recently proposed voltage-based EM immortality method for multi-segment interconnects. Numerical results on an IBM power grid and self synthesized power delivery networks show that the proposed temperature-aware EM immortality check method is much more accurate than recently proposed state of the art EM immortality method. 
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  5. Electromigration (EM) analysis for complicated interconnects requires the solving of partial differential equations, which is expensive. In this paper, we propose a fast transient hydrostatic stress analysis for EM failure assessment for multi-segment interconnects using generative adversarial networks (GANs). Our work is inspired by the image synthesis and feature of generative deep neural networks. The stress evaluation of multi-segment interconnects, modeled by partial differential equations, can be viewed as time-varying 2D-images-to-image problem where the input is the multi-segment interconnects topology with current densities and the output is the EM stress distribution in those wire segments at the given aging time. We show that the conditional GAN can be exploited to attend the temporal dynamics for modeling the time-varying dynamic systems like stress evolution over time. The resulting algorithm, called {\it EM-GAN}, can quickly give accurate stress distribution of a general multi-segment wire tree for a given aging time, which is important for full-chip fast EM failure assessment. Our experimental results show that the EM-GAN shows 6.6\% averaged error compared to COMSOL simulation results with orders of magnitude speedup. It also delivers $8.3 \times$ speedup over state-of-the-art analytic based EM analysis solver. 
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  6. In this paper, we propose a novel accuracy-reconfigurable stochastic computing (ARSC) framework for dynamic reliability and power management. Different than the existing stochastic computing works, where the accuracy versus power/energy trade-off is carried out in the design time, the new ARSC design can change accuracy or bit-width of the data in the run-time so that it can accommodate the long-term aging effects by slowing the system clock frequency at the cost of accuracy while maintaining the throughput of the computing. We validate the ARSC concept on a discrete cosine transformation (DCT) and inverse DCT designs for image compressing/decompressing applications, which are implemented on Xilinx Spartan-6 family XC6SLX45 platform. Experimental results show that the new design can easily mitigate the long-term aging-induced effects by accuracy trade-off while maintaining the throughput of the whole computing process using simple frequency scaling. We further show that one-bit precision loss for the input data, which translated to 3.44dB of the accuracy loss in term of Peak Signal to Noise Ratio (PSNR) for images, we can sufficiently compensate the NBTI induced aging effects in 10 years while maintaining the pre-aging computing throughput of 7.19 frames per second. At the same time, we can save 74\% power consumption by 10.67dB of accuracy loss. The proposed ARSC computing framework also allows much aggressive frequency scaling, which can lead to order of magnitude power savings compared to the traditional dynamic voltage and frequency scaling (DVFS) techniques. 
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