Modern FPGAs support dual-output LUT to reduce the area of FPGA designs. Several existing works explored the use of dual-output LUTs in approximate computing. However, they are limited to small-scale arithmetic circuits. To address this issue, we propose QUADOL, a quality-driven approximate logic synthesis (ALS) method leveraging dual-output LUTs for modern FPGAs. It can approximately merge two single-output LUTs into a dual-output LUT. The selection of LUTs for approximate merging is formulated as a maximum matching problem to maximize area savings. To further enhance existing ALS methods, we also propose QUADOL+, a generic framework to integrate QUADOL into existing ALS methods. Experimental results showed that QUADOL+ achieves significant area reduction over prior works.

Modern online platforms configure multiple pages to accommodate diverse user needs. This multi-page architecture inherently establishes a two-stage interaction paradigm between the user and the platform: (1) Stage I: page navigation, navigating users to a specific page and (2) Stage II: in-page interaction, where users engage with customized content within the specific page. While the majority of research has been focusing on the sequential recommendation task that improves users' feedback in Stage II, there has been little investigation on how to achieve better page navigation in Stage I. To fill this gap, we formally define the task of Personalized Landing Page Modeling (PLPM) into the field of recommender systems: Given a user upon app entry, the goal of PLPM is to proactively select the most suitable landing page from a set of candidates (e.g., functional tabs, content channels, or aggregation pages) to optimize the short-term metric Page Drop-off Ratio (PDR) and the long-term user engagement and satisfaction metrics, while adhering to industrial constraints. Additionally, we propose KLAN (Kuaishou Landing-page Adaptive Navigator), a hierarchical solution framework designed to provide personalized landing pages under the formulation of PLPM. KLAN comprises three key components: (1) KLAN-ISP captures inter-day static page preference; (2) KLAN-IIT captures intra-day dynamic interest transitions and (3) KLAN-AM adaptively integrates both components for optimal navigation decisions. Extensive online experiments conducted on the Kuaishou platform demonstrate the effectiveness of KLAN, obtaining +0.205% and +0.192% improvements on in Daily Active Users (DAU) and user Lifetime (LT). Our KLAN is ultimately deployed on the online platform at full traffic, serving hundreds of millions of users. To promote further research in this important area, we will release our dataset and code upon paper acceptance.

The rapid proliferation of short videos on social media platforms presents unique challenges and opportunities for recommendation systems. Users exhibit diverse preferences, and the responses resulting from different strategies often conflict with one another, potentially exhibiting inverse correlations between metrics such as watch time and video view counts. Existing uplift models face limitations in handling the heterogeneous multi-treatment scenarios of short-video recommendations, often failing to effectively capture both the synergistic and individual causal effects of different strategies. Furthermore, traditional fixed-weight approaches for balancing these responses lack personalization and can result in biased decision-making. To address these issues, we propose a novel Heterogeneous Multi-treatment Uplift Modeling (HMUM) framework for trade-off optimization in short-video recommendations. HMUM comprises an Offline Hybrid Uplift Modeling (HUM) module, which captures the synergistic and individual effects of multiple strategies, and an Online Dynamic Decision-Making (DDM) module, which estimates the weights of different user responses in real-time for personalized decision-making. Evaluated on two public datasets, an industrial dataset, and online A/B experiments on the Kuaishou platform, our model demonstrated superior offline performance and significant improvements in key metrics. It is now fully deployed on the platform, benefiting hundreds of millions of users.

Approximate multipliers (AppMults) are widely employed in deep neural network (DNN) accelerators to reduce the area, delay, and power consumption. However, the inaccuracies of AppMults degrade DNN accuracy, necessitating a retraining process to recover accuracy. A critical step in retraining is computing the gradient of the AppMult, i.e., the partial derivative of the approximate product with respect to each input operand. Conventional methods approximate this gradient using that of the accurate multiplier (AccMult), often leading to suboptimal retraining results, especially for AppMults with relatively large errors. To address this issue, we propose a difference-based gradient approximation of AppMults to improve retraining accuracy. Experimental results show that compared to the state-of-the-art methods, our method improves the DNN accuracy after retraining by 4.10% and 2.93% on average for the VGG and ResNet models, respectively. Moreover, after retraining a ResNet18 model using a 7-bit AppMult, the final DNN accuracy does not degrade compared to the quantized model using the 7-bit AccMult, while the power consumption is reduced by 51%.

Large-scale recommendation systems mainly rely on sparse ID features, struggling with data sparsity. It's important to use multimodal information to assist ID learning for better performance. However, there exists two challenges: (1) distribution discrepancy between multimodal and ID makes direct integration prone to user-item mismatch; (2) slower convergence of multimodal representations compared to ID, causing optimization imbalance under a unified objective, which limits the potential of multimodal representations. In this paper, we comprehensively investigate the two problems and proposes a framework named AB-Rec to align and balance ID and multimodal representations learning for recommendation. We design three alignment tasks to fine-tune a pre-trained multimodal large language model (MLLM), which is then utilized to generate a unified multimodal representation for each item. AB-Rec aligns the distributions of ID and multimodal representations by minimizing the in-batch Wasserstein distance, and maximizes the distance between the two types of representations for the same item to avoid representation collapse. To solve the optimization imbalance, we propose a gradient modulation method that adaptively controls the optimization process by monitoring the contribution differences between ID and multimodal representations. Finally, we conduct extensive offline experiments on four datasets and an A/B test on an online video platform, demonstrating the effectiveness and scalability of our proposed method.

In real-world recommendation scenarios, users engage with items through various types of behaviors. Leveraging diversified user behavior information for learning can enhance the recommendation of target behaviors (e.g., buy), as demonstrated by recent multi-behavior methods. The mainstream multi-behavior recommendation framework consists of two steps: fusion and prediction. Recent approaches utilize graph neural networks for multi-behavior fusion and employ multi-task learning paradigms for joint optimization in the prediction step, achieving significant success. However, these methods have limited perspectives on multi-behavior fusion, which leads to inaccurate capture of user behavior patterns in the fusion step. Moreover, when using multi-task learning for prediction, the relationship between the target task and auxiliary tasks is not sufficiently coordinated, resulting in negative information transfer. To address these problems, we propose a novel multi-behavior recommendation framework based on the combinatorial optimization perspective, named COPF. Specifically, we treat multi-behavior fusion as a combinatorial optimization problem, imposing different constraints at various stages of each behavior to restrict the solution space, thus significantly enhancing fusion efficiency (COGCN). In the prediction step, we improve both forward and backward propagation during the generation and aggregation of multiple experts to mitigate negative transfer caused by differences in both feature and label distributions (DFME). Comprehensive experiments on three real-world datasets indicate the superiority of COPF. Further analyses also validate the effectiveness of the COGCN and DFME modules. Our code is available at https://github.com/1918190/COPF.

Recommender systems filter contents/items valuable to users by inferring preferences from user features and historical behaviors. Mainstream approaches follow the learning-to-rank paradigm, which focus on discovering and modeling item topics (e.g., categories), and capturing user preferences on these topics based on historical interactions. However, this paradigm often neglects the modeling of user characteristics and their social roles, which are logical confounders influencing the correlated interest and user preference transition. To bridge this gap, we introduce the user role identification task and the behavioral logic modeling task that aim to explicitly model user roles and learn the logical relations between item topics and user social roles. We show that it is possible to explicitly solve these tasks through an efficient integration framework of Large Language Model (LLM) and recommendation systems, for which we propose TagCF. On the one hand, TagCF exploits the (Multi-modal) LLM's world knowledge and logic inference ability to extract realistic tag-based virtual logic graphs that reveal dynamic and expressive knowledge of users, refining our understanding of user behaviors. On the other hand, TagCF presents empirically effective integration modules that take advantage of the extracted tag-logic information, augmenting the recommendation performance. We conduct both online experiments and offline experiments with industrial and public datasets as verification of TagCF's effectiveness, and we empirically show that the user role modeling strategy is potentially a better choice than the modeling of item topics. Additionally, we provide evidence that the extracted logic graphs are empirically a general and transferable knowledge that can benefit a wide range of recommendation tasks. Our code is available in https://github.com/Code2Q/TagCF.

The popularity of short video applications has brought new opportunities and challenges to video recommendation.In addition to the traditional ranking-based pipeline, industrial solutions usually introduce additional distribution management components to guarantee a diverse and content-rich user experience.However, existing solutions are either non-personalized or fail to generalize well to the ever-changing user preferences.Inspired by the success of uplift modeling in online marketing, we attempt to implement uplift modeling in the video recommendation scenario to mitigate the problems.However, we face two main challenges when migrating the technique: 1) the complex-response causal relation in distribution management problem, and 2) the modeling of long-term and real-time user preferences.To address these challenges, we correspond each treatment to a specific adjustment of the distribution over video types, then propose a Coarse-to-fine Dynamic Uplift Modeling (CDUM) framework for real-time video recommendation scenarios.Specifically, CDUM consists of two modules, a coarse-grained module that utilizes the offline features of users to model their long-term preferences, and a fine-grained * Both authors contributed equally to this research.

Approximate computing is an emerging paradigm for designing error-resilient applications. It reduces circuit area, power, and delay at the cost of introducing errors. This article proposes a powerful technique, termed approximate resubstitution (AppResub), to approximately simplify the circuit. AppResub replaces a node’s function with a simpler approximate function on existing nodes in the circuit to reduce the hardware cost. Leveraging AppResub, an efficient flow for approximate logic synthesis (ALS) is developed by iteratively applying a set of promising AppResubs for circuit simplification. To evaluate errors caused by a set of AppResubs, a novel error model capable of efficiently computing an error upper bound is used to smartly apply AppResubs in the ALS flow. The experimental results demonstrate that compared to a state-of-the-art method, the proposed flow further reduces 20.9% area and 21.7% delay under the mean error distance constraint, while being$400\times $faster. The code of our flow is open-source.

Approximate computing is an effective computing paradigm to reduce area, delay, and power for error-tolerant applications. Average error is a widely-used metric for approximate circuits, measuring the average deviation between the outputs of exact and approximate circuits. This paper proposes VACSEM, a formal method to verify average errors in approximate circults using simulatlon-enhanced model counting. VACSEM leverages circuit structure information and logic simulation to speed up verification. Experimental results show that VACSEM is on average 35 x faster than the state-of-the-art method.

Hardware Trojan (HT) poses a critical security threat to integrated circuits, which can change circuit functionality or leak sensitive data. HTs are typically activated under low-probability conditions by exploiting "rare signals" in logic circuits. In this paper, we propose RareLS, rarity-reducing logic synthesis for mitigating HT threats. Specifically, RareLS reduces the number of rare signals through rarity-oriented technology-independent optimization and technology mapping. Experimental results show that RareLS reduces rare signals by 63.4% on average, with a small overhead of 4.0% in area, 1.9% in delay, and 6.2% in power. Moreover, RareLS complicates HT insertion for attackers by reducing HT trigger logic by 92.94%, and aids defenders in detecting HTs by shortening the test length by more than 80.83%.

In precision agriculture systems, camera networks serve as indispensable visual information acquisition devices. However, due to cost constraints and the complexity of field image information, coupled with environmental variability, image quality degradation often occurs, resulting in low-quality visual images. An effective solution for super-resolution (SR) is urgently needed. Traditional image super-resolution methods perform well under laboratory conditions, but their effectiveness is limited in real field environments due to the inability to accurately simulate actual degradation processes. To address this issue, a degradation framework tailored to field scenes is proposed, which involves precise estimation of image degradation features and the design of targeted degradation algorithms. Additionally, drawing inspiration from the powerful global dependency modeling capability of Transformers, an efficient hybrid architecture is designed that combines the local characteristics of convolutional neural networks with the global attention mechanism of Transformers, aiming to improve image reconstruction quality while reducing computational complexity. Experimental results demonstrate that the proposed method outperforms comparative methods, achieving better subjective and objective quality evaluations.

In the field of dual-target Cross-Domain Recommendation (DTCDR), improving the performance in both the information sparse domain and rich domain has been a mainstream research trend. However, prior embedding-based methods are insufficient to adequately describe the dynamics of user actions and items across domains. Moreover, previous efforts frequently lacked a comprehensive investigation of the entire domain distributions. This paper proposes a novel framework entitled Wasserstein Cross-Domain Recommendation (WCDR) that captures uncertainty in Wasserstein space to address above challenges. In this framework, we abstract user/item actions as Elliptical Gaussian distributions and divide them into local-intrinsic and global-domain parts. To further model the domain diversity, we adopt shared-specific pattern for global-domain distributions and present Masked Domain-aware Sub-distribution Aggregation (MDSA) module to produce informative and diversified global-domain distributions, which incorporates attention-based aggregation method and masking strategy that alleviates negative transfer issues. Extensive experiments on two public datasets and one business dataset are conducted. Experimental results demonstrate the superiority of WCDR over state-of-the-art methods.

Multi-types of behaviors (e.g., clicking, carting, purchasing, etc.) widely exist in most real-world recommendation scenarios, which are beneficial to learn users’ multi-faceted preferences. As dependencies are explicitly exhibited by the multiple types of behaviors, effectively modeling complex behavior dependencies is crucial for multi-behavior prediction. The state-of-the-art multi-behavior models learn behavior dependencies indistinguishably with all historical interactions as input. However, different behaviors may reflect different aspects of user preference, which means that some irrelevant interactions may play as noises to the target behavior to be predicted. To address the aforementioned limitations, we introduce multi-interest learning to the multi-behavior recommendation. More specifically, we propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning (CKML) framework to learn shared and behavior-specific interests for different behaviors. CKML introduces two advanced modules, namely Coarse-grained Interest Extracting (CIE) and Fine-grained Behavioral Correlation (FBC) , which work jointly to capture fine-grained behavioral dependencies. CIE uses knowledge-aware information to extract initial representations of each interest. FBC incorporates a dynamic routing scheme to further assign each behavior among interests. Empirical results on three real-world datasets verify the effectiveness and efficiency of our model in exploiting multi-behavior data.

Multi-behavior recommendation algorithms aim to leverage the multiplex interactions between users and items to learn users' latent preferences. Recent multi-behavior recommendation frameworks contain two steps: fusion and prediction. In the fusion step, advanced neural networks are used to model the hierarchical correlations between user behaviors. In the prediction step, multiple signals are utilized to jointly optimize the model with a multi-task learning (MTL) paradigm. However, recent approaches have not addressed the issue caused by imbalanced data distribution in the fusion step, resulting in the learned relationships being dominated by high-frequency behaviors. In the prediction step, the existing methods use a gate mechanism to directly aggregate expert information generated by coupling input, leading to negative information transfer. To tackle these issues, we propose a Parallel Knowledge Enhancement Framework (PKEF) for multi-behavior recommendation. Specifically, we enhance the hierarchical information propagation in the fusion step using parallel knowledge (PKF). Meanwhile, in the prediction step, we decouple the representations to generate expert information and introduce a projection mechanism during aggregation to eliminate gradient conflicts and alleviate negative transfer (PME). We conduct comprehensive experiments on three real-world datasets to validate the effectiveness of our model. The results further demonstrate the rationality and effectiveness of the designed PKF and PME modules. The source code and datasets are available at https://github.com/MC-CV/PKEF.

Approximate computing is an effective computing paradigm to improve energy efficiency for error-tolerant applications. Approximate logic synthesis (ALS) methods are designed to generate approximate circuits under certain error constraints. This paper focuses on ALS methods under the maximum error constraint and proposes MECALS, a maximum error checking technique for ALS. MECALS models maximum error using partial Boolean difference and performs fast error checking with SAT sweeping. Based on MECALS, we design an efficient ALS flow. Our experimental results show that compared to a state-of-the-art ALS method, our flow is 13× faster and improves area and delay reduction by 39.2% and 26.0%, respectively.

Storing pre-computed results of frequently-used functions into lookup table (LUT) is a popular way to improve energy efficiency, but its advantage diminishes as the number of input bits increases. A recent work shows that by decomposing the target function approximately, the total LUT entries can be dramatically reduced, leading to significant energy saving. However, its heuristic approximate decomposition algorithm leads to sub-optimal approximation quality. Also, its rigid hardware architecture only supports disjoint decomposition and may have unnecessary extra power consumption sometimes. To address these issues, we develop a novel approximate decomposition algorithm based on beam search and simulated annealing, which can reduce 11.1% approximation error. We also propose a non-disjoint approximate decomposition method and two reconfigurable architectures. The first has 10.4% less error using 19.2% less energy and the second has 23.0% less error with same energy consumption compared to the state-of-the-art design.

Approximate computing is a promising computing paradigm for designing energy-efficient systems. To automatically generate approximate circuits, many local iterative approximate logic synthesis (ALS) methods have been proposed. They need to specify a particular local approximation change and apply it to modify the local structure of a circuit in each round. This will lose some global optimization opportunities, thus, degrading circuit quality. In this paper, we propose DASALS, a differentiable archltecture search-driven ALS method, to directly search the whole circuit structure to obtain the approximate circuits with better circuit quality-accuracy trade-off. DASALS is based on a proper continuous relaxation of the discrete search space of ALS and an efficient gradient descent-based search algorithm. The experimental results show that compared with a state-of-the-art method, DASALS on average reduces the area-delay product by 10.82% and mean square error by 10.93%.

Various types of user behaviors are recorded in most real-world recommendation scenarios. To fully utilize the multi-behavior information, the exploration of multiplex interaction among them is essential. Many multi-task learning based multi-behavior methods are proposed recently to use multiple types of supervision signals and perform information transfer among them. Despite the great successes, these methods fail to design prediction tasks comprehensively, leading to insufficient utilization of multi-behavior correlative information. Besides, these methods are either based on the weighting of expert information extracted from the coupled input or modeling of information transfer between multiple behavior levels through task-specific extractors, which are usually accompanied by negative transfer phenomenon1. To address the above problems, we propose a multi-behavior recommendation framework, called Hierarchical Projection Enhanced Multi-behavior Recommendation (HPMR). The key module, Projection-based Transfer Network (PTN), uses the projection mechanism to "explicitly" model the correlations of upstream and downstream behaviors, refines the upstream behavior representations, and fully uses the refined representations to enhance the learning of downstream tasks. Offline experiments on public and industrial datasets and online A/B test further verify the effectiveness of HPMR in modeling the associations from upstream to downstream and alleviating the negative transfer. The source code and datasets are available at https://github.com/MC-CV/HPMR.

Multi-types of user behavior data (e.g., clicking, adding to cart, and purchasing) are recorded in most real-world recommendation scenarios, which can help to learn users’ multi-faceted preferences. However, it is challenging to explore multi-behavior data due to the unbalanced data distribution and sparse target behavior, which lead to the inadequate modeling of high-order relations when treating multi-behavior data “as features” and gradient conflict in multi-task learning when treating multi-behavior data “as labels”. In this paper, we propose CIGF, a Compressed Interaction Graph based Framework, to overcome the above limitations. Specifically, we design a novel Compressed Interaction Graph Convolution Network (CIGCN) to model instance-level high-order relations explicitly. To alleviate the potential gradient conflict when treating multi-behavior data “as labels”, we propose a Multi-Expert with Separate Input (MESI) network with separate input on the top of CIGCN for multi-task learning. Comprehensive experiments on three large-scale real-world datasets demonstrate the superiority of CIGF.

Approximate computing is an emerging paradigm for error-tolerant applications. By introducing a reasonable amount of inaccuracy, both the area and delay of a circuit can be reduced significantly. To produce approximate circuits automatically, many approximate logic synthesis (ALS) algorithms are proposed. However, they mainly focus on area reduction and are not optimal in reducing the circuit delay. In this article, we propose HEDALS, a Highly Efficient Delay-driven ALS framework, which supports various types of local approximate changes (LACs), circuit representations, and average error metrics. To reduce delay, HEDALS builds a critical error graph (CEG) consisting of nodes on the critical paths and error information, and finds an optimized set of LACs in the CEG by either a maximum flow-based method or a priority cut-based method. The resulting set of LACs is applied to shorten all critical paths simultaneously so that the circuit delay is reduced. Besides, the simultaneous application of multiple LACs also makes HEDALS extremely fast. Compared to a state-of-the-art method, on average, HEDALS can reduce the circuit delay by 32.3%, while being$167\times $faster. The code of HEDALS is made open-source.

Approximate computing is an emerging computing paradigm to design energy-efficient systems. Many greedy approximate logic synthesis (ALS) methods have been proposed to automatically synthesize approximate circuits. They typically need to consider all local approximate changes (LACs) in each iteration of the ALS flow to select the best one, which is time-consuming. In this paper, we propose SEALS, a Sensitivity-driven Efficient ALS method to speed up a greedy ALS flow. SEALS centers around a newly proposed concept called sensitivity, which enables a fast and accurate error estimation method and an efficient method to filter out unpromising LACs. SEALS can handle any statistical error metric. The experimental results show that it outperforms a state-of-the-art ALS method in runtime by 12X to 15X without reducing circuit quality.

Approximate computing is an emerging strategy to improve the energy efficiency of many error-tolerant applications. To design an approximate circuit automatically, many approximate logic synthesis (ALS) methods have been proposed, among which many are greedy. To improve the synthesis quality of these greedy methods, one key is to calculate the errors of all candidate approximate transformations accurately. However, the traditional simulation-based method is time consuming. Instead, many existing methods just perform quick but inaccurate error estimation. In this work, to improve both the accuracy and runtime of error estimation, we propose VECBEE, a versatile efficiency–accuracy configurable batch error estimation method for greedy ALS. It is based on Monte Carlo simulation and an efficient technique to capture whether a signal change due to an introduced approximation will be propagated to each primary output. VECBEE is generally applicable to any statistical error measurement, such as error rate and average error magnitude, and any graph-based circuit representation. It allows a flexible tradeoff between the error estimation accuracy and the runtime, while even the fully accurate version is much faster than the traditional simulation-based method. We apply VECBEE to two representative greedy ALS methods and demonstrate its effectiveness in generating better approximate circuits. The code of VECBEE is made open source.

A popular way to implement an arithmetic function is through a lookup table (LUT), which stores the pre-computed outputs for all the inputs. However, its size grows exponentially with the number of input bits. In this work, targeting at computing kernels of error-tolerant applications, we propose DALTA, a reconfigurable decomposition-based approximate lookup table architecture, to approximately implement those kernels with dramatically reduced size. We also propose integer linear programming-based approximate decomposition methods to map a given function to the architecture. Our architecture features with low energy consumption and high speed. The experimental results show that our architecture achieves energy and latency savings by 56.5% and 92.4%, respectively, over the state-of-the-art approximate LUT architecture.

Approximate computing is an emerging circuit design paradigm. It improves the energy efficiency of circuits by introducing some errors. Recent works propose to apply approximate multipliers to design low-power neural network (NN) accelerators. Different from existing methods, in this paper, we advocate a method that integrates approximate logic synthesis (ALS) into the design loop of low-power NN accelerators. ALS automatically synthesizes a good approximate circuit and can take input distribution into consideration. With the help of ALS, the NN computation pattern can be exploited to design an approximate multiplier that fits better with the NN. The experimental results show that the proposed method can generate an extremely small approximate multiplier with area only 4.2% of the accurate version, while it can still achieve a high accuracy of 97.9% for LeNet-5 on MNIST dataset.

Multitask learning has proven to be useful in the field of machine learning when additional knowledge is available to help a prediction task. We adapt this paradigm to develop autotuning frameworks, where the objective is to find the optimal performance parameters of an application code that is treated as a black-box function. Furthermore, we combine multitask learning with multi-objective tuning and incorporation of coarse performance models to enhance the tuning capability. The proposed framework is parallelized and applicable to any application, particularly exascale applications with a small number of function evaluations. Compared with other state-of-the-art single-task learning frameworks, the proposed framework attains up to 2.8X better code performance for at least 80% of all tasks using up to 2048 cores.

Approximate computing is an emerging design technique for error-resilient applications. It improves circuit area, power, and delay at the cost of introducing some errors. Approximate logic synthesis (ALS) is an automatic process to produce approximate circuits. This paper proposes approximate resubstitution with approximate care set and uses it to build a simulation-based ALS flow. The experimental results demonstrate that the proposed method saves 7%-18% area compared to state-of-the-art methods. The code of ALSRAC is made open-source.

With the downscaling of CMOS technology, the circuit design margin becomes more and more tight due to wider guardband, which is required to counteract the severer transistor aging and variations. Thus, reliability-enhanced circuit design is urgently needed to reduce the guardband. In this paper, a reliability-enhanced design framework based on approximate synthesis is proposed to completely eliminate the aging guardband. It mainly includes two key parts: first, a forward reliability simulation flow supporting statistical static timing analysis (SSTA) is performed to estimate the path failure rates after aging; if the timing constraints are not satisfied, then a backward delay-driven approximate logic synthesis flow will perform approximate local changes on the critical paths to reduce the delay until the reliability requirement is finally satisfied and no aging guardband is needed. The results show that the approximate circuit has a smaller aged delay than the original circuit, so that the path failure rates are significantly decreased. It indicates that the proposed design flow can convert the timing errors that have fatal impact on applications, into negligible error on low-significance bits to improve the resilience of circuits, which provides a new perspective of reliability-enhanced design at nanoscale.

Approximate computing is an emerging paradigm for error-tolerant applications. By introducing a reasonable amount of inaccuracy, both the area and delay of a circuit can be reduced significantly. To synthesize approximate circuits automatically, many approximate logic synthesis (ALS) algorithms have been proposed. However, they mainly focus on area reduction and are not optimal in reducing the delay of the circuits. In this paper, we propose DALS, a delay-driven ALS framework. DALS works on the AND-inverter graph (AIG) representation of a circuit. It supports a wide range of approximate local changes and some commonly-used error metrics, including error rate and mean error distance. In order to select an optimal set of nodes in the AIG to apply approximate local changes, DALS establishes a critical error network (CEN) from the AIG and formulates a maximum flow problem on the CEN. Our experimental results on a wide range of benchmarks show that DALS produces approximate circuits with significantly reduced delays.

Abstract We present a GPU‐based streaming algorithm to perform high‐resolution and accurate cloth simulation. We map all the components of cloth simulation pipeline, including time integration, collision detection, collision response, and velocity updating to GPU‐based kernels and data structures. Our algorithm perform intra‐object and inter‐object collisions, handles contacts and friction, and is able to accurately simulate folds and wrinkles. We describe the streaming pipeline and address many issues in terms of obtaining high throughput on many‐core GPUs. In practice, our algorithm can perform high‐fidelity simulation on a cloth mesh with 2M triangles using 3GB of GPU memory. We highlight the parallel performance of our algorithm on three different generations of GPUs. On a high‐end NVIDIA Tesla K20c, we observe up to two orders of magnitude performance improvement as compared to a single‐threaded CPU‐based algorithm, and about one order of magnitude improvement over a 16‐core CPU‐based parallel implementation.