A High-Performance Scalable Shared-Memory SVD Processor Architecture Based on Jacobi Algorithm and Batcher’s Sorting Network
| Autor: | Seyed Mohamad Reza Shahshahani, Hamid Reza Mahdiani |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Signal processing
Memory hierarchy Computer science 020208 electrical & electronic engineering 02 engineering and technology Parallel computing Microarchitecture symbols.namesake Jacobi eigenvalue algorithm Shared memory Scalability Singular value decomposition 0202 electrical engineering electronic engineering information engineering symbols Sorting network Electrical and Electronic Engineering |
| Zdroj: | IEEE Transactions on Circuits and Systems I: Regular Papers. 67:1912-1924 |
| ISSN: | 1558-0806 1549-8328 |
| DOI: | 10.1109/tcsi.2020.2973249 |
| Popis: | Eigenvalue Decomposition (EVD) and Singular Value Decomposition (SVD) are two crucial transformations in many signal processing applications. The main drawback of these algorithms is their computationally intensive nature which prevents them to be efficiently exploited in high-performance, real-time and mobile applications. By extracting the inherent parallelism of the Jacobi SVD, a new parallel data distribution and access pattern for this algorithm is proposed first. Based on the proposed parallel data distribution, a novel shared-memory architecture is then proposed to support EVD/SVD computation in a high-performance and scalable manner. A new Multistage Interconnection Network based on Batcher’s odd-even merge sorting network is developed and exploited in the architecture to preserve its performance and scalability by simultaneously connecting different numbers of processing elements to the system memory hierarchy in a parallel conflict-free manner. The proposed architecture can be configured to compute EVD/SVD of matrices of arbitrary size, with different numbers of processing elements achieving a linear speed-up. The synthesis results in a 90 nm technology show that the system with one, two, and four processing elements achieves a throughput of 1.81, 3.63, and 7.26 million EVD/SVD’s per second, respectively with a frequency of 813 MHz for an $8\times 8$ matrix. |
| Databáze: | OpenAIRE |
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