Compute-in-memory (CiM) for edge AI inference operates under strict memory and energy constraints. While unstructured pruning reduces model size and computation, efficiently deploying the sparse weights on CiM’s dense, regular arrays remains challenging. Existing studies either incur high indexing overhead by storing per-element indexing metadata, or achieve limited compression by relying on scarce structural patterns within unstructured weights. The column packing method, which avoids the high overhead of per-element indexing and offers rich compression potential, shows promise to reconcile unstructured sparsity with CiM’s regular compute pattern, but its direct application to CiM is hindered by heuristic grouping algorithms that either yield suboptimal compression or sacrifice model accuracy.To bridge this gap and unlock the potential of column packing for CiM, this study presents EGO, an algorithm-hardware co-designed framework. EGO overcomes the inefficiency of heuristic grouping by introducing a combinatorially optimized grouping algorithm, which formulates column packing as minimum-cost graph matching. A digital CiM architecture is co-designed with the EGO column grouping formulation, which features a custom Sparsity Processing Unit (SPU) to enable efficient activation routing while preserving CiM’s dense and regular dataflow. Circuit-level simulations show that EGO achieves 1.4–3.7x average improvement in energy efficiency and 1.2–1.8x average improvement in area efficiency compared to previous state-of-the-art methods.

Long-context inference has become a central focus in recent self-regressive Transformer research. However, challenges still remain in performing decode stage due to the memory bandwidth bottleneck of attention mechanisms and the substantial memory overhead associated with KV cache. Although attention sparsity has been proposed as a potential solution, conventional sparsity methods that rely on heuristic algorithms often suffer from accuracy degradation when applied to ultra-long sequences. To break through the dilemma between accuracy-performance and bandwidth-capacity, this work proposes DIAS, a distancebased irregular attention sparsity approach with processing-inmemory (PIM) architecture. DIAS employs approximate topK attention (AKAttention) scores through graph-based search to improve inference efficiency while maintaining accuracy. Furthermore, a scalable tree-like PIM (TreePIM) architecture is introduced to achieve both memory capacity and bandwidth improvement by isolating enormous memory access for KV cache into the PIM units. Evaluations on various configurations of DIAS for Longbench with Llama3-405B models with 1 M sequence length show up to 75 times speedup compared with the state-of-the-art LLM accelerator, with accuracy drop of less than $1 \%$. Index Terms-AI and Machine Learning, Architecture & System Design

Codebook design plays a crucial role in non-orthogonal sparse code multiple access technology. In this paper, a mother constellation constructed by the Eisenstein integer unit circle in the complex plane is proposed, and the power imbalance and dimensionality reduction are introduced into the codebook design. Three optimization algorithms are used to maximize the minimum Euclidean distance (MED) of superimposed codewords on the resource element as the objective function, and the optimal solution of the rotation angle is finally obtained, so as to obtain the three expected codebooks. Simulation results demonstrate that the proposed codebooks have smaller bit error rate (BER) and better performance than the four benchmark codebooks provided under the condition of a Gaussian channel.

Abstract Precision agriculture benefits from point set registration, which can monitor plant health and growth in real time, promote the precise application of fertilizers and pesticides, and provide technical support for achieving sustainable development of agriculture. In this work, we propose a robust point set registration method for precision agriculture based on L*a*b* color guidance, bidirectional search and Cauchy distribution. First, the L*a*b* color guidance is applied to establish accurate correspondences between agricultural RGB‐D data. Second, the bidirectional nearest neighbor search strategy between point sets improves the reliability of establishing correspondences and broadens the convergence domain of the algorithm. Third, Cauchy distribution is utilized as an energy function for noise suppression, which further improves the robustness of the algorithm in dealing with complex vegetation scenes. Finally, results of ablation and simulation experiments indicate that the proposed registration algorithm can achieve more accurate and robust alignment results than other classic and state‐of‐the‐art point cloud registration algorithms to achieve monitoring and comparison of plant growth.

Hypernasality is an abnormal resonance in human speech production, especially in patients with craniofacial anomalies such as cleft palate.In clinical application, hypernasality estimation is crucial in cleft palate diagnosis, as its results determine the subsequent surgery and additional speech therapy.Therefore, designing an automatic hypernasality assessment method will facilitate speech-language pathologists to make precise diagnoses.Existing methods for hypernasality estimation only conduct acoustic analysis based on low-resource cleft palate dataset, by using statistical or neural network-based features.In this paper, we propose a novel approach that uses automatic speech recognition model to improve hypernasality estimation.Specifically, we first pre-train an encoder-decoder framework in an automatic speech recognition (ASR) objective by using speech-to-text dataset, and then fine-tune ASR encoder on the cleft palate dataset for hypernasality estimation.Benefiting from such design, our model for hypernasality estimation can enjoy the advantages of ASR model: 1) compared with low-resource cleft palate dataset, the ASR task usually includes large-scale speech data in the general domain, which enables better model generalization; 2) the text annotations in ASR dataset guide model to extract better acoustic features.Experimental results on two cleft palate datasets demonstrate that our method achieves superior performance compared with previous approaches.

We propose a robust algorithm for aligning rigid, noisy, and partially overlapping red green blue-depth (RGB-D) point clouds. To address the problems of data degradation and uneven distribution, we offer three strategies to increase the robustness of the iterative closest point (ICP) algorithm. First, we introduce a salient object detection (SOD) method to extract a set of points with significant structural variation in the foreground, which can avoid the unbalanced proportion of foreground and background point sets leading to the local registration. Second, registration algorithms that rely only on structural information for alignment cannot establish the correct correspondences when faced with the point set with no significant change in structure. Therefore, a bidirectional color distance (BCD) is designed to build precise correspondence with bidirectional search and color guidance. Third, the maximum correntropy criterion (MCC) and trimmed strategy are introduced into our algorithm to handle with noise and outliers. We experimentally validate that our algorithm is more robust than previous algorithms on simulated and real-world scene data in most scenarios and achieve a satisfying 3-D reconstruction of indoor scenes.

Point cloud registration a challenging task in situations with poor initial value and scenarios with limited geometric structure. In these cases, the correct correspondence between two point clouds is unknown and difficult to establish. To cope with this problem, the semantic of partial points is introduced in this paper. Firstly, the semantic information is used to find more reasonable correspondence, i.e. semantic point pairs. Secondly, we formulate a novel objective function to integrate the matching error of semantic point pairs as guidance of registration. Thirdly, a hyperparameter is applied to balance the confidence of semantic point pairs. At last, a novel algorithm under the ICP framework is presented to optimize the rigid transformation iteratively. The evaluation of KITTI data set reveals the robustness and accuracy of our method in the complex scenes mentioned above.

In this study, the authors present a novel individual retrieval method based on oral cavity point cloud data and correntropy‐based registration algorithm. Since the three‐dimensional oral cavity data contains a large amount of noise and outliers, it may lead to a decrease in registration accuracy, which affects the accuracy of retrieval rate. Therefore, the authors introduce the correntropy into the rigid registration algorithm to solve this problem. Then, they filter the matched point cloud data and then use the mean squared error to judge the individual differences of the model data. Finally, the accurate retrieval of the oral cavity data is realised. Experimental results demonstrate the proposed retrieval three‐dimensional model algorithm can be successfully searched under different model data, which can help forensics use the characteristics of biological individuals to accurately search and identify, and improve recognition efficiency.

Iterative closest point (ICP) algorithm, as its accuracy and efficiency, is widely used in rigid registration. However, ICP algorithm is easily failed when point sets lack of structure variety, such as semicircles. To solve this problem, a precise point set registration method for RGB-D data is proposed. Firstly, the color information provides a new information for registration, and the correntropy is introduced to deal with the noises and outliers. With color assisted and correntropy, a more robust objective function is built. Secondly, a variant ICP algorithm is used to deal with optimization problem via multiple iterations. Finally, as shown in the experimental results and scene reconstruction, our method obtains more precise results than other ICP algorithms.

As an important problem in point set registration, partial registration has been solved by some variants of Iterative Closest Point (ICP) algorithm under good initial values. However, the initial parameters remained to be solved for partial registration. This paper presents a parameter initialization algorithm based on matching triangles for partial registration. Experimental results demonstrate that the proposed initialization method can find an appropriate initial transformation for next accurate registration, even the initial rotation angle between two sets is large. Based on the initialization of two point sets, the partial registration can be accomplished by auto trimmed ICP (ATICP) algorithm.