Integer Linear Programming (ILP) is widely used for solving real-world optimization problems, including network routing, map routing, and traffic scheduling. However, ILP algorithms are sparse and branch-intensive, making them inefficient on conventional CPUs and GPUs. Prior work has shown that large-scale ILP problems can require tens of hours of execution time even on massively parallel systems, limiting their applicability to time-sensitive decision-making workloads. Existing ILP solvers such as Gurobi employ software-level optimizations to handle sparsity on CPUs, but still face throughput limitations. GPU-based ILP solvers are also constrained because GPUs are not well suited for sparse and branch-heavy workloads, leading to thread divergence, under-utilization of streaming multiprocessors, and frequent host-device interactions. This paper presents SPARK, a sparsity-aware, reuse-aware, energy-efficient, reconfigurable near-cache ILP accelerator. SPARK repurposes the existing L1 cache in CPUs to provide near-cache acceleration with minimal hardware overhead of approximately 1.4\% of the CPU area. The architecture performs near-cache sparsity detection and sparsity-aware computation to reduce insignificant computations and data movement energy. SPARK also exploits computational reuse patterns in ILP algorithms to improve parallelism and efficiency. The proposed design supports both sparse and dense ILPs as well as Linear Programs (LPs). Evaluations on real-world workloads from MIPLIB 2017 show that SPARK achieves up to 15x and 20x performance improvement, and up to 152x and 740x energy reduction compared to AMD Zen3 CPUs and NVIDIA Tesla V100 GPUs, respectively, for sparse ILPs. For sparse LPs, SPARK achieves 7-17x performance improvement and 103-250x energy reduction over CPU and GPU baselines, demonstrating the broad applicability of the proposed architecture.

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