Computing-In-Memory Neural Network Accelerators for Safety-Critical Systems: Can Small Device Variations Be Disastrous?

Computing-in-Memory (CiM) architectures based on emerging non-volatile memory (NVM) devices have demonstrated great potential for deep neural network (DNN) acceleration thanks to their high energy efficiency. However, NVM devices suffer from various non-idealities, especially device-to-device variations due to fabrication defects and cycle-to-cycle variations due to the stochastic behavior of devices. As such, the DNN weights actually mapped to NVM devices could deviate significantly from the expected values, leading to large performance degradation. To address this issue, most existing works focus on maximizing average performance under device variations. This objective would work well for general-purpose scenarios. But for safety-critical applications, the worst-case performance must also be considered. Unfortunately, this has been rarely explored in the literature. In this work, we formulate the problem of determining the worst-case performance of CiM DNN accelerators under the impact of device variations. We further propose a method to effectively find the specific combination of device variation in the high-dimensional space that leads to the worst-case performance. We find that even with very small device variations, the accuracy of a DNN can drop drastically, causing concerns when deploying CiM accelerators in safety-critical applications. Finally, we show that surprisingly none of the existing methods used to enhance average DNN performance in CiM accelerators are very effective when extended to enhance the worst-case performance, and further research down the road is needed to address this problem

U-SWIM: Universal Selective Write-Verify for Computing-in-Memory Neural Accelerators

Architectures that incorporate Computing-in-Memory (CiM) using emerging non-volatile memory (NVM) devices have become strong contenders for deep neural network (DNN) acceleration due to their impressive energy efficiency. Yet, a significant challenge arises when using these emerging devices: they can show substantial variations during the weight-mapping process. This can severely impact DNN accuracy if not mitigated. A widely accepted remedy for imperfect weight mapping is the iterative write-verify approach, which involves verifying conductance values and adjusting devices if needed. In all existing publications, this procedure is applied to every individual device, resulting in a significant programming time overhead. In our research, we illustrate that only a small fraction of weights need this write-verify treatment for the corresponding devices and the DNN accuracy can be preserved, yielding a notable programming acceleration. Building on this, we introduce USWIM, a novel method based on the second derivative. It leverages a single iteration of forward and backpropagation to pinpoint the weights demanding write-verify. Through extensive tests on diverse DNN designs and datasets, USWIM manifests up to a 10x programming acceleration against the traditional exhaustive write-verify method, all while maintaining a similar accuracy level. Furthermore, compared to our earlier SWIM technique, USWIM excels, showing a 7x speedup when dealing with devices exhibiting non-uniform variations

Optimal route design of electric transit networks considering travel reliability

Travel reliability is the most essential determinant for operating the transit system and improving its service level. In this study, an optimization model for the electric transit route network design problem is proposed, under the precondition that the locations of charging depots are predetermined. Objectives are to pursue maximum travel reliability and meanwhile control the total cost within a certain range. Constraints about the bus route and operation are also considered. A Reinforcement Learning Genetic Algorithm is developed to solve the proposed model. Two case studies including the classic Mandl\u27s road network and a large road network in the context of Zhengzhou city are conducted to demonstrate the effectiveness of the proposed model and the solution algorithm. Results suggest that the proposed methodology is helpful for improving the travel reliability of the transit network with minimal cost increase

The Cycle Spinning-based Sharp Frequency Localized Contourlet Transform for Image Denoising

Prof.YAN, as corresponding author, is advisor of Mr.Xiaobo QU.Contourlet transform provides flexible number of directions and captures the intrinsic geometrical structure of images. The efficient directional filter banks with low redundancy of contourlet are very attractive for image processing. However, non-ideal filters are used in the original contourlet transform, especially when combined with laplacian pyramid, which results in pseudo-Gibbs phenomena around singularities for image denoising. Sharp frequency localized contourlet transform (SFLCT) is a new construction contourlet to overcome this drawback by replacing the laplacian pyramid with a new multiscale decomposition which significantly improve the denoising performance than the original form. Unfortunately, the downsampling of SFLCT makes it lack translation invariance. Thus, we employ a cycle spinning (CS) method to improve the denoising performance of SFLCT, named as cycle spinning based SFLCT (CS-SFLCT), by averaging out the translation dependence. Experimental results demonstrate that the proposed CS-SFLCT outperforms SFLCT, contourlet and cycle spinning-based contourlet for denoising in terms of PSNR and in visual effects.This paper is supported by Navigation Science Foundation of China (No.05F07001) and National Natural Science Foundation of China (No.60472081)

Surface Albedo Variation and Its Influencing Factors over Dongkemadi Glacier, Central Tibetan Plateau

Glacier albedo plays a critical role in surface-atmosphere energy exchange, the variability of which influences glacier mass balance as well as water resources. Dongkemadi glacier in central Tibetan Plateau was selected as study area; this research used field measurements to verify Landsat TM-derived albedo and MOD10A1 albedo product and then analyzed the spatiotemporal variability of albedo over the glacier according to them, as well as its influence factors and the relationship with glacier mass balance. The spatial distribution of glacier albedo in winter did not vary with altitude and was determined by terrain shield, whereas, in summer, albedo increased with altitude and was only influenced by terrain shield at accumulation zone. During 2000–2009, albedo in summer decreased at a rate of 0.0052 per year and was influenced by air temperature and precipitation levels, whereas albedo in winter increased at a rate of 0.0045 per year, influenced by the level and frequency of precipitation. The annual variation of albedo in summer during 2000–2012 has the high relative to that of glacier mass balance measurement, which indicates that glacier albedo in the ablation period can be considered as a proxy for glacier mass balance