We propose a novel Greedy Graph Cut (GGC) algorithm to address the graph partitioning problem. The algorithm begins by treating each data point as an individual cluster and iteratively merges cluster pairs that maximize the reduction in the global objective function until the desired number of clusters is achieved. We provide a theoretical proof of the monotonic convergence of the objective function values throughout this process. To improve computational efficiency, the algorithm restricts merging operations to adjacent clusters, resulting in a computational complexity that scales nearly linearly with the sample size. A significant advantage of our greedy approach is its deterministic nature, which ensures consistent results across multiple runs. This stands in contrast to many existing algorithms that are sensitive to random initialization effects. We demonstrate the effectiveness of the proposed algorithm by applying it to the Normalized Cut (N-Cut) problem, a well-studied variant of graph partitioning. Extensive experimental results show that GGC consistently outperforms the conventional two-stage optimization approach-which involves eigendecomposition followed by k-means clustering-in solving the N-Cut problem. Furthermore, comparative analyses reveal that GGC achieves superior performance compared to several state-of-the-art clustering algorithms.

Convolutional Neural Networks (CNNs) can benefit from the computational reductions provided by the Winograd minimal filtering algorithm and weight pruning. However, harnessing the potential of both methods simultaneously introduces complexity in designing pruning algorithms and accelerators. Prior studies aimed to establish regular sparsity patterns in the Winograd domain, but they were primarily suited for small tiles, with domain transformation dictating the sparsity ratio. The irregularities in data access and domain transformation pose challenges in accelerator design, especially for larger Winograd tiles. This paper introduces “Winols,” an innovative algorithm-hardware co-design strategy that emphasizes the strengths of the large-tiling Winograd algorithm. Through a spatial-to-Winograd relevance degree evaluation, we extensively explore domain transformation and propose a cross-domain pruning technique that retains sparsity across both spatial and Winograd domains. To compress pruned weight matrices, we invent a relative column encoding scheme. We further design an FPGA-based accelerator for CNN models with large Winograd tiles and sparse matrix-vector operations. Evaluations indicate our pruning method achieves up to 80% weight tile sparsity in the Winograd domain without compromising accuracy. Our Winols accelerator outperforms dense accelerator by a factor of 31.7× in inference latency. When compared with prevailing sparse Winograd accelerators, Winols reduces latency by an average of 10.9×, and improves DSP and energy efficiencies by over 5.6× and 5.7×, respectively. When compared with the CPU and GPU platform, Winols accelerator with tile size 8× 8 achieves 24.6× and 2.84× energy efficiency improvements, respectively.