1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.AR2021
Solving Large Top-K Graph Eigenproblems with a Memory and Compute-optimized FPGA Design
Francesco Sgherzi, Alberto Parravicini, Marco Siracusa +1
Large-scale eigenvalue computations on sparse matrices are a key component of graph analytics techniques based on spectral methods. In such applications, an exhaustive computation…
cs.AR2021★ 1 cited
Scaling up HBM Efficiency of Top-K SpMV for Approximate Embedding Similarity on FPGAs
Alberto Parravicini, Luca Giuseppe Cellamare, Marco Siracusa +1
Top-K SpMV is a key component of similarity-search on sparse embeddings. This sparse workload does not perform well on general-purpose NUMA systems that employ traditional caching…