Recently, printed circuit board (PCB) placement has emerged as a significant challenge since the scale of PCB designs has rapidly enlarged. Furthermore, the presence of various types of constraints with differing tolerance priorities hampers the automation of PCB layout design, necessitating substantial manual effort. To address this problem, we introduce a novel agent-based framework that automatically generates PCB layouts meeting industrial constraints through user interactions. This framework includes two main agents: a reinforcement learning (RL)-based agent for layout inference and fine-tuning, and a large language model (LLM)-based agent for interactive optimization. Experimental results on 17 industrial tasks show that our framework outperforms other state-of-the-art methods.

In the domain of chip design, hardware description languages (HDLs) play a pivotal role. However, due to the inherent complexity of HDLs and the scarcity of high-quality debugging resources, HDL bug fixing remains a challenging and time-consuming task, even for seasoned engineers. Consequently, there is a pressing need to develop automated HDL code debugging models, which can alleviate the burden on hardware engineers. Despite the strong capabilities of large language models (LLMs) in generating, completing, and debugging software code, their utilization in the specialized field of HDL debugging has been limited and, to date, has not yielded satisfactory results. In this paper, we propose an LLM-assisted HDL debugging framework, namely HDLdebugger, which consists of HDL debugging data generation via a reverse engineering approach, a search engine for retrieval-augmented generation, and a retrieval-augmented LLM fine-tuning approach. Through the integration of these components, HDLdebugger can automate and streamline HDL debugging for chip design. Our comprehensive experiments, conducted on an HDL code dataset sourced from Industry, reveal that HDLdebugger outperforms 13 cutting-edge LLM baselines, displaying exceptional effectiveness in HDL code debugging.

High-level synthesis is an effective methodology that accelerates early-stage circuit design. The optimization of HLS directives has been a critical yet challenging endeavor, with prevailing research primarily concentrating on custom feature engineering and dedicated model designs. However, these conventional approaches often fall short of fully harnessing the intricate latent information embedded within raw HLS directives, potentially limiting the scope and efficiency of optimization processes. In response to these challenges, this article pioneers the integration of large language model (LLM) into the HLS optimization workflow, leveraging their capabilities as both sophisticated feature extractors and autonomous agents. This application of LLM marks a significant departure from traditional methods, introducing a more nuanced and effective strategy for navigating the complex landscape of HLS directive optimization, enabling a more efficient exploration of the design space and prioritization of search strategies. Specifically, our approach makes a significant improvement to the Pareto frontier in directive design, enabling a more rapid and efficient design space exploration. This demonstrates not only an increase in optimization performance but also a decrease in computational overhead, thereby promising significant time savings in the circuit design process. This work not only enhances the current state of HLS directive optimization but also makes new avenues for the application of language models in the field of EDA. Our work makes the following key achievements: We propose an LLM-based framework for effective HLS directives design space exploration; We utilize the prior knowledge of LLM and fine-tune an LLM for HLS directives optimization; Empirical results demonstrate this LLM-based approach’s effectiveness. Specifically, we obtain 15% improvement on the normalized ADRS metric, demonstrating superior performance with limited sampling steps compared with current leading algorithms.

Over the past few years, large language models (LLMs) have demonstrated remarkable performance and versatility across a variety of complex tasks. However, their deployment has been challenged by their substantial model size and computational requirements. Pruning is a effective approach to make the model parameters sparse, thereby acquire inference acceleration. While not everyone requires training or fine-tuning large models, the diverse range of applications necessitates the deployment of LLMs on different devices. Model pruning and compression have emerged as areas of deep research interest to address these challenges. In consideration of versatility and practicality, we have designed a hardware-aware pruning process for general-purpose hardware/edge devices to enable efficient deployment and inference of LLMs. Instead of considering sparse ratio alone, we are motivated to design a pruning framework that incorporates genuine inference speed-up sensitivity from each pruning structure. Moreover, our framework breaks the layer-by-layer pruning setting and fuse several layers into one pruning stage to allow cross-layer optimization. Apart from that, we hold pragmatism by conducting compilation optimization during pruning. This step is critical because most sparsity patterns barely show distinct speed acceleration with corresponding dataflow and memory optimization. Our process operates within a post-training framework, obviating the need for additional training and thereby reducing resource requirements, while ensuring diverse inference speed and accuracy requirements on hardware.

Vision-Language models (VLMs) pre-trained on large corpora have demonstrated notable success across a range of downstream tasks. In light of the rapidly increasing size of pre-trained VLMs, parameter-efficient transfer learning (PETL) has garnered attention as a viable alternative to full fine-tuning. One such approach is the adapter, which introduces a few trainable parameters into the pre-trained models while preserving the original parameters during adaptation. In this paper, we present a novel modeling framework that recasts adapter tuning after attention as a graph message passing process on attention graphs, where the projected query and value features and attention matrix constitute the node features and the graph adjacency matrix, respectively. Within this framework, tuning adapters in VLMs necessitates handling heterophilic graphs, owing to the disparity between the projected query and value space. To address this challenge, we propose a new adapter architecture, p-adapter, which employs p-Laplacian message passing in Graph Neural Networks (GNNs). Specifically, the attention weights are re-normalized based on the features, and the features are then aggregated using the calibrated attention matrix, enabling the dynamic exploitation of information with varying frequencies in the heterophilic attention graphs. We conduct extensive experiments on different pre-trained VLMs and multi-modal tasks, including visual question answering, visual entailment, and image captioning. The experimental results validate our method's significant superiority over other PETL methods. Our code is available at https://github.com/wuhy68/p-Adapter/.

Knowledge distillation aims at transferring knowledge from the teacher model to the student one by aligning their distributions. Feature-level distillation often uses L2 distance or its variants as the loss function, based on the assumption that outputs follow normal distributions. This poses a significant challenge when distribution gaps are substantial since this loss function ignores the variance term. To address the problem, we propose to decompose the transfer objective into small parts and optimize it progressively. This process is inspired by diffusion models from which the noise distribution is mapped to the target distribution step by step. However, directly employing diffusion models is impractical in the distillation scenario due to its heavy reverse process. To overcome this challenge, we adopt the structural re-parameterization technique to generate multiple student features to approximate the teacher features sequentially. The multiple student features are combined linearly in inference time without extra cost. We present extensive experiments performed on various transfer scenarios, such as CNN-to-CNN and Transformer-to-CNN, that validate the effectiveness of our approach.

Existing works focus on fixed-size layout pattern generation, while the more practical free-size pattern generation receives limited attention. In this paper, we propose ChatPattern, a novel Large-Language-Model (LLM) powered framework for flexible pattern customization. ChatPattern utilizes a two-part system featuring an expert LLM agent and a highly controllable layout pattern generator. The LLM agent can interpret natural language requirements and operate design tools to meet specified needs, while the generator excels in conditional layout generation, pattern modification, and memory-friendly patterns extension. Experiments on challenging pattern generation setting shows the ability of ChatPattern to synthesize high-quality large-scale patterns.

Register Transfer Level (RTL) code optimization is crucial for enhancing the efficiency and performance of digital circuits during early synthesis stages. Currently, optimization relies heavily on manual efforts by skilled engineers, often requiring multiple iterations based on synthesis feedback. In contrast, existing compiler-based methods fall short in addressing complex designs. This paper introduces RTLRewriter, an innovative framework that leverages large models to optimize RTL code. A circuit partition pipeline is utilized for fast synthesis and efficient rewriting. A multi-modal program analysis is proposed to incorporate vital visual diagram information as optimization cues. A specialized search engine is designed to identify useful optimization guides, algorithms, and code snippets that enhance the model's ability to generate optimized RTL. Additionally, we introduce a Cost-aware Monte Carlo Tree Search (C-MCTS) algorithm for efficient rewriting, managing diverse retrieved contents and steering the rewriting results. Furthermore, a fast verification pipeline is proposed to reduce verification cost. To cater to the needs of both industry and academia, we propose two benchmarking suites: the long Rewriter benchmark, targeting complex scenarios with extensive circuit partitioning, optimization trade-offs, and verification challenges, and the short Rewriter benchmark, designed for a wider range of scenarios and patterns. Our comparative analysis with established compilers such as Yosys and E-graph demonstrates significant improvements, highlighting the benefits of integrating large models into the early stages of circuit design. We provide our benchmarks at https://github.com/yaoxufeng/RTLRewriter-Bench.

Deep learning frameworks or compilers optimize the operators in computation graph using fixed templates via significant engineering efforts, which may miss potential optimizations such as operator fusion. Therefore, automatically implementing and optimizing the emerging new combinations of operators on a specific hardware accelerator is of importance. In this article, we introduce GTCO, a tensor compilation system designed to accelerate transformer-based vision models’ inference on GPUs. GTCO tackles the operator fusion techniques in the transformer-based model using a novel dynamic programming algorithm and proposes a search policy with new sketch generation rules for the fused batch matrix multiplication and softmax operators. Tensor programs are sampled from an effective search space, and a hardware abstraction with hierarchical mapping from tensor computation to domain-specific accelerators (Tensor Cores) is formally defined. Finally, our framework can map and transform tensor expression into efficient CUDA kernels with hardware intrinsics on GPU. Our experimental results demonstrate that GTCO improves the end-to-end execution performance by up to$1.73\times $relative to the cutting-edge deep learning library TensorRT on NVIDIA GPUs with Tensor Cores.

The integration of a complex set of Electronic Design Automation (EDA) tools to enhance interoperability is a critical concern for circuit designers. Recent advancements in large language models (LLMs) have showcased their exceptional capabilities in natural language processing and comprehension, offering a novel approach to interfacing with EDA tools. This research paper introduces ChatEDA, an autonomous agent for EDA empowered by a large language model, AutoMage, complemented by EDA tools serving as executors. ChatEDA streamlines the design flow from the Register-Transfer Level (RTL) to the Graphic Data System Version II (GDSII) by effectively managing task decomposition, script generation, and task execution. Through comprehensive experimental evaluations, ChatEDA has demonstrated its proficiency in handling diverse requirements, and our fine-tuned AutoMage model has exhibited superior performance compared to GPT-4 and other similar LLMs.

Optical proximity correction (OPC) is a widely used technique to enhance the printability of designs in various foundaries. Recently, there has been a growing interest in using rigorous numerical optimization and machine learning to improve the robustness and efficiency of OPC. Our research focuses on developing a self-adaptive OPC framework that leverages the properties of pattern distribution and repetition in design layouts to optimize the correction process. We observe that different sub-regions in a design layer have varying pattern complexities, and many patterns repeat themselves throughout the layout. By exploiting these properties, we propose a framework that adaptively selects the most suitable OPC solvers from an extensible pool to optimize the correction process for each pattern based on its complexity. This approach allows for a co-optimization of speed and accuracy. Additionally, we introduce a graph-based dynamic pattern library that reuses optimized masks for repeated patterns, further accelerating the OPC flow. Our experimental results demonstrate a significant improvement in both performance and efficiency using our proposed framework.

Domain adaptation is a promising way to ease the costly data labeling process in the era of deep learning (DL). A practical situation is partial domain adaptation (PDA), where the label space of the target domain is a subset of that in the source domain. Although existing methods yield appealing performance in PDA tasks, it is highly presumable that computation overhead exists in deep PDA models since the target is only a subtask of the original problem. In this work, PDA and model compression are seamlessly integrated into a unified training process. The cross-domain distribution divergence is reduced by minimizing a soft-weighted maximum mean discrepancy (SWMMD), which is differentiable and functions as regularization during network training. We use gradient statistics to compress the overparameterized model to identify and prune redundant channels based on the corresponding scaling factors in batch normalization (BN) layers. The experimental results demonstrate that our method can achieve comparable classification performance to state-of-the-art methods on various PDA tasks, with a significant reduction in model size and computation overhead.

Quantization is a critical technique employed across various research fields for compressing deep neural networks (DNNs) to facilitate deployment within resource-limited environments. This process necessitates a delicate balance between model size and performance. In this work, we explore knowledge distillation (KD) as a promising approach for improving quantization performance by transferring knowledge from high-precision networks to low-precision counterparts. We specifically investigate feature-level information loss during distillation and emphasize the importance of feature-level network quantization perception. We propose a novel quantization method that combines feature-level distillation and contrastive learning to extract and preserve more valuable information during the quantization process. Furthermore, we utilize the hyperbolic tangent function to estimate gradients with respect to the rounding function, which smoothens the training procedure. Our extensive experimental results demonstrate that the proposed approach achieves competitive model performance with the quantized network compared to its full-precision counterpart, thus validating its efficacy and potential for real-world applications.

Current 3D scene stylization methods transfer textures and colors as styles using arbitrary style references, lacking meaningful semantic correspondences. We introduce Reference-Based Non-Photorealistic Radiance Fields (Ref-NP R) to address this limitation. This controllable method stylizes a 3D scene using radiance fields with a single stylized 2D view as a reference. We propose a ray registration process based on the stylized reference view to obtain pseudo-ray supervision in novel views. Then we exploit semantic correspondences in content images to fill occluded regions with perceptually similar styles, resulting in non-photorealistic and continuous novel view sequences. Our experimental results demonstrate that Ref-NPR out-performs existing scene and video stylization methods regarding visual quality and semantic correspondence. The code and data are publicly available on the project page at https://ref-npr.github.io.

Space-filling curves (SFCs) act as a linearization approach to map data in higher dimensional space to lower dimensional space, which is used comprehensively in computer vision, such as image/point cloud compression, hashing and etc. Currently, researchers formulate the problem of searching for an optimal SFC to the problem of finding a single Hamiltonian circuit on the image grid graph. Existing methods adopt graph neural networks (GNN) for SFC search. By modeling the pixel grid as a graph, they first adopt GNN to predict the edge weights and then generate a minimum spanning tree (MST) based on the predictions, which is further used to construct the SFC. However, GNN-based methods suffer from high computational costs and memory footprint usage. Besides, MST generation is un-differentiable, which is infeasible to optimize via gradient descent. To remedy these issues, we propose a GNN-based SFC-search framework with a tailored algorithm that largely reduces computational cost of GNN. Additionally, we propose a siamese network learning scheme to optimize DNN-based models in an end-to-end fashion. Extensive experiments show that our proposed method outperforms both DNN-based methods and traditional SFCs, e.g. Hilbert curve, by a large margin on various benchmarks.

Unsupervised anomaly detection aims to train models with only anomaly-free images to detect and localize unseen anomalies. Previous reconstruction-based methods have been limited by inaccurate reconstruction results. This work presents a denoising model to detect and localize the anomalies with a generative diffusion model. In particular, we introduce random noise to overwhelm the anomalous pixels and obtain pixel-wise precise anomaly scores from the intermediate denoising process. We find that the KL divergence of the diffusion model serves as a better anomaly score compared with the traditional RGB space score. Furthermore, we reconstruct the features from a pre-trained deep feature extractor as our feature level score to improve localization performance. Moreover, we propose a gradient denoising process to smoothly transform an anomalous image into a normal one. Our denoising model outperforms the state-of-the-art reconstruction-based anomaly detection methods for precise anomaly localization and high-quality normal image reconstruction on the MVTec-AD benchmark.

Pursuing accurate and robust recognizers has been a long-lasting goal for scene text recognition (STR) researchers. Recently, attention-based methods have demonstrated their effectiveness and achieved impressive results on public benchmarks. The attention mechanism enables models to recognize scene text with severe visual distortions by leveraging contextual information. However, recent studies revealed that the implicit over-reliance of context leads to catastrophic out-of-vocabulary performance. On the contrary to the superior accuracy of the seen text, models are prone to misrecognize unseen text even with good image quality. We propose a novel framework, Context-based contrastive learning (ConCLR), to alleviate this issue. Our proposed method first generates characters with different contexts via simple image concatenation operations and then optimizes contrastive loss on their embeddings. By pulling together clusters of identical characters within various contexts and pushing apart clusters of different characters in embedding space, ConCLR suppresses the side-effect of overfitting to specific contexts and learns a more robust representation. Experiments show that ConCLR significantly improves out-of-vocabulary generalization and achieves state-of-the-art performance on public benchmarks together with attention-based recognizers.

Domain generalization refers to the problem of training a model from a collection of different source domains that can directly generalize to the unseen target domains. A promising solution is contrastive learning, which attempts to learn domain-invariant representations by exploiting rich semantic relations among sample-to-sample pairs from different domains. A simple approach is to pull positive sample pairs from different domains closer while pushing other negative pairs further apart. In this paper, we find that directly applying contrastive-based methods (e.g., supervised contrastive learning) are not effective in domain generalization. We argue that aligning positive sample-to-sample pairs tends to hinder the model generalization due to the significant distribution gaps between different domains. To address this issue, we propose a novel proxy-based contrastive learning method, which replaces the original sample-to-sample relations with proxy-to-sample relations, significantly alleviating the positive alignment issue. Experiments on the four standard benchmarks demonstrate the effectiveness of the proposed method. Furthermore, we also consider a more complex scenario where no ImageNet pre-trained models are provided. Our method consistently shows better performance.

Optical proximity correction (OPC) is a widely-used resolution enhancement technique (RET) for printability optimization. Recently, rigorous numerical optimization and fast machine learning are the research focus of OPC in both academia and industry, each of which complements the other in terms of robustness or efficiency. We inspect the pattern distribution on a design layer and find that different sub-regions have different pattern complexity. Besides, we also find that many patterns repetitively appear in the design layout, and these patterns may possibly share optimized masks. We exploit these properties and propose a self-adaptive OPC framework to improve efficiency. Firstly we choose different OPC solvers adaptively for patterns of different complexity from an extensible solver pool to reach a speed/accuracy co-optimization. Apart from that, we prove the feasibility of reusing optimized masks for repeated patterns and hence, build a graph-based dynamic pattern library reusing stored masks to further speed up the OPC flow. Experimental results show that our framework achieves substantial improvement in both performance and efficiency.

Abstract Precisely detecting lung nodules from original computerized tomography (CT) images is a critical technology in the earlier screening of lung cancer. Therefore, the domain of accurate detection has gradually attracted the attention of researchers. However, due to the complex characteristics of pulmonary nodules and the limitations of CT imaging property, detecting nodules with high accuracy from lung CT images is a challenging task. This article proposes an effective and robust detection network to accurately detect lung nodules by innovatively implementing a probability graph model in the candidate detection and false‐positive reduction phase. Different from previous works which use complex 3‐dimensional image information to reduce false positives, we propose two effective probability graph mechanisms, which analyze multiscale information and continuous slices (interslice changes) motion information to improve performance. We evaluated our method on an open‐source LIDC‐IDRI dataset which contains a total of 243,958 CT images and achieved high‐precision lung nodule detection results (sensitivity score of 0.945). Via introducing multiscale information and the dynamic information of the interslice, the task of lung nodule detection obtains higher precision detection results than other similar methods.

This article studies the robust deep stereo vision in autonomous driving systems and counteracting adversarial attacks. The autonomous system operation requires real-time processing of measurement data which often contain significant uncertainties and noise. Adversarial attacks have been widely studied to simulate these perturbations in recent years. To counteract the practical attacks in autonomous systems, novel methods based on simulated attacks are proposed in this article. Univariate and multivariate functions are adopted to represent the relationships between the left and right input images and the deep stereo model. A stereo regularizer is proposed to guide the model to learn the implicit relationship between the images and characterize the loss function’s local smoothness. The attacks are generated by maximizing the regularizer term to break the linearity and smoothness. The model then defends the attacks by minimizing the loss and regularization terms. Two techniques are developed in this article. The first technique,SmoothStereo, explores the basic knowledge from the physical world and smoothness, while the second technique,SmoothStereoV2, improvesSmoothStereothrough leveraging the smooth activation functions during the defense.SmoothStereoV2can learn and utilize the gradient information concerning the attacks. The gradients of the smooth activation functions can handle attacks for improving the model robustness. Numerical experiments on KITTI datasets demonstrate that the proposed methods offer superior performance.

Performance optimization is the art of continuously seeking an effective mapping between algorithm and hardware. Existing deep learning compilers or frameworks optimize the computation graph via adapting transformations manually designed by expert efforts. We argue that these methods ignore some possible graph-level optimizations, thus it is difficult to generalize to emerging deep learning models or new operators. In this work, we propose AutoGTCO, a tensor program generation system for vision tasks with the transformer architecture on GPU. Compared with existing fusion strategies, AutoGTCO explores the optimization of operator fusion in the transformer model through a novel dynamic programming algorithm. Specifically, to construct an effective search space of the sampled programs, new sketch generation rules and a search policy are proposed for the batch matrix multiplication and softmax operators in each subgraph, which are capable of fusing them into large computation units, it can then map and transform them into efficient CUDA kernels. Overall, our evaluation on three real-world transformer-based vision tasks shows that AutoGTCO improves the execution performance relative to deep learning engine TensorRT by up to 1.38 ×.

In this paper, we focus on studying robust deep stereo vision of autonomous driving systems and counteracting adversarial attacks against it. Autonomous system operation requires real-time processing of measurement data which often contain significant uncertainties and noise. Adversarial attacks have been widely studied to simulate these perturbations in recent years. To counteract these attacks in autonomous systems, a novel defense method is proposed in this paper. A stereo-regularizer is proposed to guide the model to learn the implicit relationship between the left and right images of the stereo-vision system. Univariate and multivariate functions are adopted to characterize the relationships between the two input images and the object detection model. The regularizer is then relaxed to its upper bound to improve adversarial robustness. Furthermore, the upper bound is approximated by the remainder of its Taylor expansion to improve the local smoothness of the loss surface. The model parameters are trained via adversarial training with the novel regularization term. Our method exploits basic knowledge from the physical world, i.e., the mutual constraints of the two images in the stereo-based system. As such, outliers can be detected and defended with high accuracy and efficiency. Numerical experiments demonstrate that the proposed method offers superior performance when compared with traditional adversarial training methods in state-of-the-art stereo-based 3D object detection models for autonomous vehicles.