While sparsity, a feature of data in many applications, provides optimization opportunities such as reducing unnecessary computations, data transfers, and storage, it causes several challenges, too. For instance, even in state-of-the-art sparse accelerators, sparsity can result in load imbalance; a performance bottleneck. To solve such challenges, our key insight is that if while reading/streaming compressed sparse matrices we can quickly anticipate the locations of the non-zero values in a sparse matrix, we can leverage this knowledge to accelerate processing sparse matrices. To enable this, we propose Pipirima, a lightweight prediction-based sparse accelerator. Inspired by traditional branch predictors, Pipirima uses resource-friendly simple counters to predict the patterns of non-zero values in the sparse matrices. We evaluate Pipirima based on sparse matrix vector multiplication (SpMV) and sparse matrix-dense matrix multiplication (SpMM) kernels on CSR compressed matrices derived from both scientific computing and transformer models. On average, our experiments show $6 \times$ and $4 \times$ speed up over Tensaurus for SpMM and SpMV, respectively on SuiteSparse workload. Pipirima also shows $40 \times$ speed up over ExTensor for SpMM. We achieve $8.3 \times$, $48.2 \times$ over Tensaurus and Extensor in lesser sparse transformer workloads. Piprima consumes $5.621 \mathrm{~mm}^{2}$ area and 544.93 mW power using 45 nm technology with predictor related components as the least expensive ones.

Although modern supercomputers are capable of delivering Exaflops now, they do not always achieve their peak performance. For instance, even today's high-end supercom-puters achieve only less than 5% of their peak FLOPS when running HPCG, a benchmark designed to represent real-world scientific computing programs. To improve the efficiency of the key kernels in scientific computing, such as those used in solving partial differential equations, computer architects have begun to expand the applications of domain-specific architectures (DSAs) to scientific computing. However, DSAs that often have a fixed design are not likely to be practical solutions, as one specialized solution cannot fit all the diverse scientific computing workloads, making them less effective. The challenges of hardware inef-ficiency in today's supercomputers and the ineffectiveness of DSAs are further exacerbated by sparsity, a key characteristic of scientific computing workloads. While prior studies have proposed DSA solutions for sparse computations, they too are static and not adaptable to variations in the patterns and levels of sparsity across different scientific workloads. To address these challenges and target not only the diversity of computations in such workloads but also variations in sparsity, we propose Acamar11Acamar /’ rckomarr/ is a binary star system in the constellation of Eridanus., a dynamically reconfigurable accelerator. Acamar is adaptable to various solvers across different workloads and dynamically optimizes the trade-off between resource utilization and latency for sparse computations. The adaptable design also enables selecting a solver that guarantees convergence. We evaluate Acamar based on its Vitis HLS implementation on Xilinx Alveo u55c. Our experiments show a resource utilization and latency improvement up to 3.5 x and 6 x as well as improved performance efficiency and achieved throughput over a static design and Nvidia GTX 1650 Super.